{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/task/natural-language-inference/papers/ran/1","list_of":"/task/natural-language-inference","task":"Natural Language Inference","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","n_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this task or check it against the task's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","page":1,"pages_in_order":3,"rows_per_page":100,"rows":[1,100],"of":209,"counts":{"archive_papers_tagged":1961,"with_a_code_link":821,"where_syntology_ran_a_sample":209,"not_listed_spam_title":0,"listed":1961,"listed_where_code_ran":209,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":170,"every_run_a_failure_of_syntologys_instrument":39,"listed_with_a_run_with_no_instrument_failure":170,"listed_every_run_a_failure_of_syntologys_instrument":39,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/natural-language-inference/papers/ran/1","prev":null,"next":"/task/natural-language-inference/papers/ran/2","papers":[{"url":"/paper/factcg-enhancing-fact-checkers-with-graph","slug":"factcg-enhancing-fact-checkers-with-graph","title":"FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Data","date":"2025-01-28","arxiv_id":"2501.17144","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/factcg-enhancing-fact-checkers-with-graph#ran","syntology_url":"https://syntology.ai/paper/2501.17144","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2501.17144"}},"official":{"repos":["derenlei/factcg"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/on-adversarial-robustness-and-out-of","slug":"on-adversarial-robustness-and-out-of","title":"On Adversarial Robustness and Out-of-Distribution Robustness of Large Language Models","date":"2024-12-13","arxiv_id":"2412.10535","repositories_listed":1,"syntology":{"n":13,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":13,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/on-adversarial-robustness-and-out-of#ran","syntology_url":"https://syntology.ai/paper/2412.10535","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2412.10535"}},"official":{"repos":["jordantab/llm-robustness-experiment"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/inference-and-verbalization-functions-during","slug":"inference-and-verbalization-functions-during","title":"Inference and Verbalization Functions During In-Context Learning","date":"2024-10-12","arxiv_id":"2410.09349","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/inference-and-verbalization-functions-during#ran","syntology_url":"https://syntology.ai/paper/2410.09349","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.09349"}},"official":{"repos":["junyitao/infer-then-verbalize-during-icl"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dadee-unsupervised-domain-adaptation-in-early","slug":"dadee-unsupervised-domain-adaptation-in-early","title":"DAdEE: Unsupervised Domain Adaptation in Early Exit PLMs","date":"2024-10-06","arxiv_id":"2410.04424","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dadee-unsupervised-domain-adaptation-in-early#ran","syntology_url":"https://syntology.ai/paper/2410.04424","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.04424"}},"official":{"repos":["div290/dadee"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/take-it-easy-label-adaptive-self","slug":"take-it-easy-label-adaptive-self","title":"Take It Easy: Label-Adaptive Self-Rationalization for Fact Verification and Explanation Generation","date":"2024-10-05","arxiv_id":"2410.04002","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/take-it-easy-label-adaptive-self#ran","syntology_url":"https://syntology.ai/paper/2410.04002","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.04002"}},"official":{"repos":["jingyng/label-adaptive-self-rationalization"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/anah-v2-scaling-analytical-hallucination","slug":"anah-v2-scaling-analytical-hallucination","title":"ANAH-v2: Scaling Analytical Hallucination Annotation of Large Language Models","date":"2024-07-05","arxiv_id":"2407.04693","repositories_listed":1,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/anah-v2-scaling-analytical-hallucination#ran","syntology_url":"https://syntology.ai/paper/2407.04693","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.04693"}},"official":{"repos":["open-compass/anah"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/econnli-evaluating-large-language-models-on","slug":"econnli-evaluating-large-language-models-on","title":"EconNLI: Evaluating Large Language Models on Economics Reasoning","date":"2024-07-01","arxiv_id":"2407.01212","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/econnli-evaluating-large-language-models-on#ran","syntology_url":"https://syntology.ai/paper/2407.01212","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.01212"}},"official":{"repos":["irenehere/econnli"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/seeing-the-big-through-the-small-can-llms","slug":"seeing-the-big-through-the-small-can-llms","title":"\"Seeing the Big through the Small\": Can LLMs Approximate Human Judgment Distributions on NLI from a Few Explanations?","date":"2024-06-25","arxiv_id":"2406.17600","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/seeing-the-big-through-the-small-can-llms#ran","syntology_url":"https://syntology.ai/paper/2406.17600","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.17600"}},"official":{"repos":["mainlp/mjd-estimator"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/css-contrastive-semantic-similarity-for","slug":"css-contrastive-semantic-similarity-for","title":"CSS: Contrastive Semantic Similarity for Uncertainty Quantification of LLMs","date":"2024-06-05","arxiv_id":"2406.03158","repositories_listed":1,"syntology":{"n":11,"n_ran":9,"n_constructed":0,"n_ran_checked":8,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":11,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/css-contrastive-semantic-similarity-for#ran","syntology_url":"https://syntology.ai/paper/2406.03158","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2406.03158"}},"official":{"repos":["aoshuang92/css_uq_llms"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/accurate-and-nuanced-open-qa-evaluation","slug":"accurate-and-nuanced-open-qa-evaluation","title":"Accurate and Nuanced Open-QA Evaluation Through Textual Entailment","date":"2024-05-26","arxiv_id":"2405.16702","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/accurate-and-nuanced-open-qa-evaluation#ran","syntology_url":"https://syntology.ai/paper/2405.16702","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.16702"}},"official":{"repos":["U-Alberta/QA-partial-marks"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/quantifying-and-optimizing-global","slug":"quantifying-and-optimizing-global","title":"Quantifying and Optimizing Global Faithfulness in Persona-driven Role-playing","date":"2024-05-13","arxiv_id":"2405.07726","repositories_listed":1,"syntology":{"n":7,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/quantifying-and-optimizing-global#ran","syntology_url":"https://syntology.ai/paper/2405.07726","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.07726"}},"official":{"repos":["KomeijiForce/Active_Passive_Constraint_Koishiday_2024"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/the-effect-of-model-size-on-llm-post-hoc","slug":"the-effect-of-model-size-on-llm-post-hoc","title":"The Effect of Model Size on LLM Post-hoc Explainability via LIME","date":"2024-05-08","arxiv_id":"2405.05348","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/the-effect-of-model-size-on-llm-post-hoc#ran","syntology_url":"https://syntology.ai/paper/2405.05348","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.05348"}},"official":{"repos":["henningheyen/scalability-of-llm-posthoc-explanations"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/verification-and-refinement-of-natural","slug":"verification-and-refinement-of-natural","title":"Verification and Refinement of Natural Language Explanations through LLM-Symbolic Theorem Proving","date":"2024-05-02","arxiv_id":"2405.01379","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/verification-and-refinement-of-natural#ran","syntology_url":"https://syntology.ai/paper/2405.01379","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.01379"}},"official":{"repos":["neuro-symbolic-ai/explanation_refinement"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/don-t-say-no-jailbreaking-llm-by-suppressing","slug":"don-t-say-no-jailbreaking-llm-by-suppressing","title":"Don't Say No: Jailbreaking LLM by Suppressing Refusal","date":"2024-04-25","arxiv_id":"2404.16369","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":5,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/don-t-say-no-jailbreaking-llm-by-suppressing#ran","syntology_url":"https://syntology.ai/paper/2404.16369","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.16369"}},"official":{"repos":["dsn-2024/dsn"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/adverb-is-the-key-simple-text-data","slug":"adverb-is-the-key-simple-text-data","title":"Adverb Is the Key: Simple Text Data Augmentation with Adverb Deletion","date":"2024-03-29","arxiv_id":"2403.20015","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/adverb-is-the-key-simple-text-data#ran","syntology_url":"https://syntology.ai/paper/2403.20015","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.20015"}},"official":{"repos":["c-juhwan/adverb-deletion-aug"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/generative-pretrained-structured-transformers","slug":"generative-pretrained-structured-transformers","title":"Generative Pretrained Structured Transformers: Unsupervised Syntactic Language Models at Scale","date":"2024-03-13","arxiv_id":"2403.08293","repositories_listed":2,"syntology":{"n":7,"n_ran":4,"n_constructed":2,"n_ran_checked":3,"n_instrument":1,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 2 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 1 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/generative-pretrained-structured-transformers#ran","syntology_url":"https://syntology.ai/paper/2403.08293","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.08293"}},"official":{"repos":["ant-research/structuredlm_rtdt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/fine-grained-natural-language-inference-based","slug":"fine-grained-natural-language-inference-based","title":"Fine-Grained Natural Language Inference Based Faithfulness Evaluation for Diverse Summarisation Tasks","date":"2024-02-27","arxiv_id":"2402.17630","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fine-grained-natural-language-inference-based#ran","syntology_url":"https://syntology.ai/paper/2402.17630","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.17630"}},"official":{"repos":["hjznlp/infuse"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/improving-sentence-embeddings-with-an","slug":"improving-sentence-embeddings-with-an","title":"Improving Sentence Embeddings with Automatic Generation of Training Data Using Few-shot Examples","date":"2024-02-23","arxiv_id":"2402.15132","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/improving-sentence-embeddings-with-an#ran","syntology_url":"https://syntology.ai/paper/2402.15132","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.15132"}},"official":{"repos":["lamsoma/auto_nli"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pixel-sentence-representation-learning","slug":"pixel-sentence-representation-learning","title":"Pixel Sentence Representation Learning","date":"2024-02-13","arxiv_id":"2402.08183","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":4,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/pixel-sentence-representation-learning#ran","syntology_url":"https://syntology.ai/paper/2402.08183","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.08183"}},"official":{"repos":["gowitheflow-1998/pixel-linguist"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/can-large-language-models-explain-themselves-1","slug":"can-large-language-models-explain-themselves-1","title":"Are self-explanations from Large Language Models faithful?","date":"2024-01-15","arxiv_id":"2401.07927","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/can-large-language-models-explain-themselves-1#ran","syntology_url":"https://syntology.ai/paper/2401.07927","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.07927"}},"official":{"repos":["AndreasMadsen/llm-introspection"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/model-editing-can-hurt-general-abilities-of","slug":"model-editing-can-hurt-general-abilities-of","title":"Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue","date":"2024-01-09","arxiv_id":"2401.04700","repositories_listed":1,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":11,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/model-editing-can-hurt-general-abilities-of#ran","syntology_url":"https://syntology.ai/paper/2401.04700","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2401.04700"}},"official":{"repos":["jasonforjoy/model-editing-hurt"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/building-efficient-universal-classifiers-with","slug":"building-efficient-universal-classifiers-with","title":"Building Efficient Universal Classifiers with Natural Language Inference","date":"2023-12-29","arxiv_id":"2312.17543","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/building-efficient-universal-classifiers-with#ran","syntology_url":"https://syntology.ai/paper/2312.17543","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.17543"}},"official":{"repos":["moritzlaurer/zeroshot-classifier"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/performance-trade-offs-of-watermarking-large","slug":"performance-trade-offs-of-watermarking-large","title":"Downstream Trade-offs of a Family of Text Watermarks","date":"2023-11-16","arxiv_id":"2311.09816","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/performance-trade-offs-of-watermarking-large#ran","syntology_url":"https://syntology.ai/paper/2311.09816","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.09816"}},"official":{"repos":["flair-iisc/watermark_tradeoffs"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/heuristics-driven-link-of-analogy-prompting","slug":"heuristics-driven-link-of-analogy-prompting","title":"LLMs Learn Task Heuristics from Demonstrations: A Heuristic-Driven Prompting Strategy for Document-Level Event Argument Extraction","date":"2023-11-11","arxiv_id":"2311.06555","repositories_listed":1,"syntology":{"n":6,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":6,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/heuristics-driven-link-of-analogy-prompting#ran","syntology_url":"https://syntology.ai/paper/2311.06555","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.06555"}},"official":{"repos":["hzzhou01/hd-loa-prompting"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fast-and-accurate-factual-inconsistency","slug":"fast-and-accurate-factual-inconsistency","title":"Fast and Accurate Factual Inconsistency Detection Over Long Documents","date":"2023-10-19","arxiv_id":"2310.13189","repositories_listed":1,"syntology":{"n":9,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":7,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":9,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/fast-and-accurate-factual-inconsistency#ran","syntology_url":"https://syntology.ai/paper/2310.13189","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.13189"}},"official":{"repos":["asappresearch/scale-score"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":7,"ran_from_kinds":["official"]}}},{"url":"/paper/chain-of-natural-language-inference-for","slug":"chain-of-natural-language-inference-for","title":"Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinations","date":"2023-10-06","arxiv_id":"2310.03951","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/chain-of-natural-language-inference-for#ran","syntology_url":"https://syntology.ai/paper/2310.03951","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.03951"}},"official":{"repos":["microsoft/conli_hallucination"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/modulora-finetuning-3-bit-llms-on-consumer","slug":"modulora-finetuning-3-bit-llms-on-consumer","title":"ModuLoRA: Finetuning 2-Bit LLMs on Consumer GPUs by Integrating with Modular Quantizers","date":"2023-09-28","arxiv_id":"2309.16119","repositories_listed":3,"syntology":{"n":9,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":8,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/modulora-finetuning-3-bit-llms-on-consumer#ran","syntology_url":"https://syntology.ai/paper/2309.16119","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.16119"}},"official":{"repos":["kuleshov-group/llmtools","kuleshov-group/modulora-experiment"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/augmenting-transformers-with-recursively","slug":"augmenting-transformers-with-recursively","title":"Augmenting Transformers with Recursively Composed Multi-grained Representations","date":"2023-09-28","arxiv_id":"2309.16319","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/augmenting-transformers-with-recursively#ran","syntology_url":"https://syntology.ai/paper/2309.16319","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.16319"}},"official":{"repos":["ant-research/structuredlm_rtdt"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/calm-a-multi-task-benchmark-for-comprehensive","slug":"calm-a-multi-task-benchmark-for-comprehensive","title":"CALM : A Multi-task Benchmark for Comprehensive Assessment of Language Model Bias","date":"2023-08-24","arxiv_id":"2308.12539","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":2,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/calm-a-multi-task-benchmark-for-comprehensive#ran","syntology_url":"https://syntology.ai/paper/2308.12539","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.12539"}},"official":{"repos":["vipulgupta1011/calm"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/do-multilingual-language-models-think-better","slug":"do-multilingual-language-models-think-better","title":"Do Multilingual Language Models Think Better in English?","date":"2023-08-02","arxiv_id":"2308.01223","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/do-multilingual-language-models-think-better#ran","syntology_url":"https://syntology.ai/paper/2308.01223","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.01223"}},"official":{"repos":["juletx/self-translate"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pac-neural-prediction-set-learning-to","slug":"pac-neural-prediction-set-learning-to","title":"Selective Generation for Controllable Language Models","date":"2023-07-18","arxiv_id":"2307.09254","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":5,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/pac-neural-prediction-set-learning-to#ran","syntology_url":"https://syntology.ai/paper/2307.09254","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.09254"}},"official":{"repos":["ml-postech/selective-generation"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/from-key-points-to-key-point-hierarchy","slug":"from-key-points-to-key-point-hierarchy","title":"From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarization","date":"2023-06-06","arxiv_id":"2306.03853","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":0,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/from-key-points-to-key-point-hierarchy#ran","syntology_url":"https://syntology.ai/paper/2306.03853","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.03853"}},"official":{"repos":["ibm/kpa-hierarchy"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-study-of-situational-reasoning-for-traffic","slug":"a-study-of-situational-reasoning-for-traffic","title":"A Study of Situational Reasoning for Traffic Understanding","date":"2023-06-05","arxiv_id":"2306.02520","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":2,"n_instrument":3,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/a-study-of-situational-reasoning-for-traffic#ran","syntology_url":"https://syntology.ai/paper/2306.02520","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.02520"}},"official":{"repos":["saccharomycetes/text-based-traffic-understanding"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/biomedgpt-a-unified-and-generalist-biomedical","slug":"biomedgpt-a-unified-and-generalist-biomedical","title":"BiomedGPT: A Generalist Vision-Language Foundation Model for Diverse Biomedical Tasks","date":"2023-05-26","arxiv_id":"2305.17100","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":5,"n_pointer_only":7,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/biomedgpt-a-unified-and-generalist-biomedical#ran","syntology_url":"https://syntology.ai/paper/2305.17100","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.17100"}},"official":{"repos":["taokz/biomedgpt"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/contrastive-learning-of-sentence-embeddings","slug":"contrastive-learning-of-sentence-embeddings","title":"Contrastive Learning of Sentence Embeddings from Scratch","date":"2023-05-24","arxiv_id":"2305.15077","repositories_listed":2,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/contrastive-learning-of-sentence-embeddings#ran","syntology_url":"https://syntology.ai/paper/2305.15077","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.15077"}},"official":{"repos":["hkust-nlp/syncse","sjtu-lit/syncse"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/can-large-language-models-infer-and-disagree","slug":"can-large-language-models-infer-and-disagree","title":"Can Large Language Models Capture Dissenting Human Voices?","date":"2023-05-23","arxiv_id":"2305.13788","repositories_listed":1,"syntology":{"n":28,"n_ran":24,"n_constructed":0,"n_ran_checked":24,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":1,"n_no_contract":23,"n_pointer_only":0,"phrase":"24 ran (of which 0 constructed an object rather than computing a result; 24 with no instrument failure: 0 honoured, 1 violated, 23 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/can-large-language-models-infer-and-disagree#ran","syntology_url":"https://syntology.ai/paper/2305.13788","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.13788"}},"official":{"repos":["xfactlab/emnlp2023-llm-disagreement"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":14,"n_unverified":4,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/enhancing-cross-lingual-natural-language-1","slug":"enhancing-cross-lingual-natural-language-1","title":"Enhancing Cross-lingual Natural Language Inference by Soft Prompting with Multilingual Verbalizer","date":"2023-05-22","arxiv_id":"2305.12761","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/enhancing-cross-lingual-natural-language-1#ran","syntology_url":"https://syntology.ai/paper/2305.12761","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.12761"}},"official":{"repos":["thu-bpm/softmv"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/rwkv-reinventing-rnns-for-the-transformer-era","slug":"rwkv-reinventing-rnns-for-the-transformer-era","title":"RWKV: Reinventing RNNs for the Transformer Era","date":"2023-05-22","arxiv_id":"2305.13048","repositories_listed":14,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/rwkv-reinventing-rnns-for-the-transformer-era#ran","syntology_url":"https://syntology.ai/paper/2305.13048","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.13048"}},"official":{"repos":["BlinkDL/RWKV-LM","blinkdl/chatrwkv"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/scene-self-labeled-counterfactuals-for","slug":"scene-self-labeled-counterfactuals-for","title":"SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples","date":"2023-05-13","arxiv_id":"2305.07984","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/scene-self-labeled-counterfactuals-for#ran","syntology_url":"https://syntology.ai/paper/2305.07984","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.07984"}},"official":{"repos":["deqingfu/scene"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/automatic-evaluation-of-attribution-by-large","slug":"automatic-evaluation-of-attribution-by-large","title":"Automatic Evaluation of Attribution by Large Language Models","date":"2023-05-10","arxiv_id":"2305.06311","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/automatic-evaluation-of-attribution-by-large#ran","syntology_url":"https://syntology.ai/paper/2305.06311","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.06311"}},"official":{"repos":["osu-nlp-group/attrscore"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pouf-prompt-oriented-unsupervised-fine-tuning","slug":"pouf-prompt-oriented-unsupervised-fine-tuning","title":"POUF: Prompt-oriented unsupervised fine-tuning for large pre-trained models","date":"2023-04-29","arxiv_id":"2305.00350","repositories_listed":1,"syntology":{"n":9,"n_ran":4,"n_constructed":0,"n_ran_checked":0,"n_instrument":4,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":9,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 4 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/pouf-prompt-oriented-unsupervised-fine-tuning#ran","syntology_url":"https://syntology.ai/paper/2305.00350","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.00350"}},"official":{"repos":["korawat-tanwisuth/pouf"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/receval-evaluating-reasoning-chains-via","slug":"receval-evaluating-reasoning-chains-via","title":"ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness","date":"2023-04-21","arxiv_id":"2304.10703","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/receval-evaluating-reasoning-chains-via#ran","syntology_url":"https://syntology.ai/paper/2304.10703","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.10703"}},"official":{"repos":["archiki/receval"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/uncertainty-aware-natural-language-inference","slug":"uncertainty-aware-natural-language-inference","title":"Uncertainty-Aware Natural Language Inference with Stochastic Weight Averaging","date":"2023-04-10","arxiv_id":"2304.04726","repositories_listed":1,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/uncertainty-aware-natural-language-inference#ran","syntology_url":"https://syntology.ai/paper/2304.04726","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.04726"}},"official":{"repos":["helsinki-nlp/uncertainty-aware-nli"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/wice-real-world-entailment-for-claims-in","slug":"wice-real-world-entailment-for-claims-in","title":"WiCE: Real-World Entailment for Claims in Wikipedia","date":"2023-03-02","arxiv_id":"2303.01432","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/wice-real-world-entailment-for-claims-in#ran","syntology_url":"https://syntology.ai/paper/2303.01432","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.01432"}},"official":{"repos":["ryokamoi/wice"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/chatgpt-jack-of-all-trades-master-of-none","slug":"chatgpt-jack-of-all-trades-master-of-none","title":"ChatGPT: Jack of all trades, master of none","date":"2023-02-21","arxiv_id":"2302.10724","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/chatgpt-jack-of-all-trades-master-of-none#ran","syntology_url":"https://syntology.ai/paper/2302.10724","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.10724"}},"official":{"repos":["clarin-pl/chatgpt-evaluation-01-2023"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/compositional-exemplars-for-in-context","slug":"compositional-exemplars-for-in-context","title":"Compositional Exemplars for In-context Learning","date":"2023-02-11","arxiv_id":"2302.05698","repositories_listed":1,"syntology":{"n":7,"n_ran":5,"n_constructed":1,"n_ran_checked":2,"n_instrument":3,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"5 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 1 honoured, 0 violated, 1 with no contract checked; 3 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/compositional-exemplars-for-in-context#ran","syntology_url":"https://syntology.ai/paper/2302.05698","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.05698"}},"official":{"repos":["hkunlp/icl-ceil"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":1,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/exploring-the-benefits-of-training-expert","slug":"exploring-the-benefits-of-training-expert","title":"Exploring the Benefits of Training Expert Language Models over Instruction Tuning","date":"2023-02-07","arxiv_id":"2302.03202","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/exploring-the-benefits-of-training-expert#ran","syntology_url":"https://syntology.ai/paper/2302.03202","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.03202"}},"official":{"repos":["joeljang/elm"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official","unlocated"]}}},{"url":"/paper/hungry-hungry-hippos-towards-language","slug":"hungry-hungry-hippos-towards-language","title":"Hungry Hungry Hippos: Towards Language Modeling with State Space Models","date":"2022-12-28","arxiv_id":"2212.14052","repositories_listed":3,"syntology":{"n":15,"n_ran":7,"n_constructed":0,"n_ran_checked":3,"n_instrument":4,"n_unverified":8,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 1 honoured, 0 violated, 2 with no contract checked; 4 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/hungry-hungry-hippos-towards-language#ran","syntology_url":"https://syntology.ai/paper/2212.14052","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.14052"}},"official":{"repos":["hazyresearch/h3"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/disco-distilling-phrasal-counterfactuals-with","slug":"disco-distilling-phrasal-counterfactuals-with","title":"DISCO: Distilling Counterfactuals with Large Language Models","date":"2022-12-20","arxiv_id":"2212.10534","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/disco-distilling-phrasal-counterfactuals-with#ran","syntology_url":"https://syntology.ai/paper/2212.10534","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.10534"}},"official":{"repos":["eric11eca/disco"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/cross-lingual-retrieval-augmented-prompt-for","slug":"cross-lingual-retrieval-augmented-prompt-for","title":"Cross-Lingual Retrieval Augmented Prompt for Low-Resource Languages","date":"2022-12-19","arxiv_id":"2212.09651","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/cross-lingual-retrieval-augmented-prompt-for#ran","syntology_url":"https://syntology.ai/paper/2212.09651","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.09651"}},"official":{"repos":["ercong21/parc"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/mabel-attenuating-gender-bias-using-textual","slug":"mabel-attenuating-gender-bias-using-textual","title":"MABEL: Attenuating Gender Bias using Textual Entailment Data","date":"2022-10-26","arxiv_id":"2210.14975","repositories_listed":2,"syntology":{"n":6,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/mabel-attenuating-gender-bias-using-textual#ran","syntology_url":"https://syntology.ai/paper/2210.14975","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.14975"}},"official":{"repos":["princeton-nlp/mabel"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/r-2-f-a-general-retrieval-reading-and-fusion","slug":"r-2-f-a-general-retrieval-reading-and-fusion","title":"R$^2$F: A General Retrieval, Reading and Fusion Framework for Document-level Natural Language Inference","date":"2022-10-22","arxiv_id":"2210.12328","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/r-2-f-a-general-retrieval-reading-and-fusion#ran","syntology_url":"https://syntology.ai/paper/2210.12328","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.12328"}},"official":{"repos":["phoenixsecularbird/r2f"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ask-me-anything-a-simple-strategy-for","slug":"ask-me-anything-a-simple-strategy-for","title":"Ask Me Anything: A simple strategy for prompting language models","date":"2022-10-05","arxiv_id":"2210.02441","repositories_listed":3,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ask-me-anything-a-simple-strategy-for#ran","syntology_url":"https://syntology.ai/paper/2210.02441","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.02441"}},"official":{"repos":["hazyresearch/ama_prompting"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/sequence-learning-using-equilibrium","slug":"sequence-learning-using-equilibrium","title":"Sequence Learning Using Equilibrium Propagation","date":"2022-09-14","arxiv_id":"2209.09626","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/sequence-learning-using-equilibrium#ran","syntology_url":"https://syntology.ai/paper/2209.09626","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2209.09626"}},"official":{"repos":["neurocomplab-psu/eqprop-seqlearning"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/llm-int8-8-bit-matrix-multiplication-for","slug":"llm-int8-8-bit-matrix-multiplication-for","title":"LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale","date":"2022-08-15","arxiv_id":"2208.07339","repositories_listed":4,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/llm-int8-8-bit-matrix-multiplication-for#ran","syntology_url":"https://syntology.ai/paper/2208.07339","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.07339"}},"official":{"repos":["timdettmers/bitsandbytes"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["listed"]}}},{"url":"/paper/alexatm-20b-few-shot-learning-using-a-large","slug":"alexatm-20b-few-shot-learning-using-a-large","title":"AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model","date":"2022-08-02","arxiv_id":"2208.01448","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/alexatm-20b-few-shot-learning-using-a-large#ran","syntology_url":"https://syntology.ai/paper/2208.01448","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.01448"}},"official":null}},{"url":"/paper/n-grammer-augmenting-transformers-with-latent-1","slug":"n-grammer-augmenting-transformers-with-latent-1","title":"N-Grammer: Augmenting Transformers with latent n-grams","date":"2022-07-13","arxiv_id":"2207.06366","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/n-grammer-augmenting-transformers-with-latent-1#ran","syntology_url":"https://syntology.ai/paper/2207.06366","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.06366"}},"official":{"repos":["tensorflow/lingvo"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/logical-reasoning-with-span-predictions-span","slug":"logical-reasoning-with-span-predictions-span","title":"Logical Reasoning with Span-Level Predictions for Interpretable and Robust NLI Models","date":"2022-05-23","arxiv_id":"2205.11432","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":2,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/logical-reasoning-with-span-predictions-span#ran","syntology_url":"https://syntology.ai/paper/2205.11432","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.11432"}},"official":{"repos":["joestacey/snli_logic"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/unifying-language-learning-paradigms","slug":"unifying-language-learning-paradigms","title":"UL2: Unifying Language Learning Paradigms","date":"2022-05-10","arxiv_id":"2205.05131","repositories_listed":2,"syntology":{"n":16,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":15,"n_pointer_only":0,"phrase":"15 ran (of which 0 constructed an object rather than computing a result; 15 with no instrument failure: 0 honoured, 0 violated, 15 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/unifying-language-learning-paradigms#ran","syntology_url":"https://syntology.ai/paper/2205.05131","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.05131"}},"official":{"repos":["google-research/google-research"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/natural-language-inference-with-self","slug":"natural-language-inference-with-self","title":"Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims","date":"2022-05-05","arxiv_id":"2205.02596","repositories_listed":0,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/natural-language-inference-with-self#ran","syntology_url":"https://syntology.ai/paper/2205.02596","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.02596"}},"official":null}},{"url":"/paper/polyglot-prompt-multilingual-multitask","slug":"polyglot-prompt-multilingual-multitask","title":"Polyglot Prompt: Multilingual Multitask PrompTraining","date":"2022-04-29","arxiv_id":"2204.14264","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":2,"n_instrument":2,"n_unverified":0,"n_honours":2,"n_violates":0,"n_no_contract":0,"n_pointer_only":4,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 2 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/polyglot-prompt-multilingual-multitask#ran","syntology_url":"https://syntology.ai/paper/2204.14264","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.14264"}},"official":{"repos":["jinlanfu/polyglot_prompt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/making-the-most-of-text-semantics-to-improve","slug":"making-the-most-of-text-semantics-to-improve","title":"Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing","date":"2022-04-21","arxiv_id":"2204.09817","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 3 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/making-the-most-of-text-semantics-to-improve#ran","syntology_url":"https://syntology.ai/paper/2204.09817","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.09817"}},"official":{"repos":["microsoft/hi-ml"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/palm-scaling-language-modeling-with-pathways-1","slug":"palm-scaling-language-modeling-with-pathways-1","title":"PaLM: Scaling Language Modeling with Pathways","date":"2022-04-05","arxiv_id":"2204.02311","repositories_listed":7,"syntology":{"n":37,"n_ran":32,"n_constructed":16,"n_ran_checked":24,"n_instrument":8,"n_unverified":5,"n_honours":2,"n_violates":1,"n_no_contract":21,"n_pointer_only":0,"phrase":"32 ran (of which 16 constructed an object rather than computing a result; 24 with no instrument failure: 2 honoured, 1 violated, 21 with no contract checked; 8 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/palm-scaling-language-modeling-with-pathways-1#ran","syntology_url":"https://syntology.ai/paper/2204.02311","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.02311"}},"official":null}},{"url":"/paper/few-shot-learning-with-siamese-networks-and-1","slug":"few-shot-learning-with-siamese-networks-and-1","title":"Few-Shot Learning with Siamese Networks and Label Tuning","date":"2022-03-28","arxiv_id":"2203.14655","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/few-shot-learning-with-siamese-networks-and-1#ran","syntology_url":"https://syntology.ai/paper/2203.14655","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.14655"}},"official":{"repos":["symanto-research/few-shot-learning-label-tuning"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/zs4ie-a-toolkit-for-zero-shot-information","slug":"zs4ie-a-toolkit-for-zero-shot-information","title":"ZS4IE: A toolkit for Zero-Shot Information Extraction with simple Verbalizations","date":"2022-03-25","arxiv_id":"2203.13602","repositories_listed":2,"syntology":{"n":12,"n_ran":7,"n_constructed":0,"n_ran_checked":4,"n_instrument":3,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/zs4ie-a-toolkit-for-zero-shot-information#ran","syntology_url":"https://syntology.ai/paper/2203.13602","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.13602"}},"official":{"repos":["bbn-e/zs4ie"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/datamux-data-multiplexing-for-neural-networks","slug":"datamux-data-multiplexing-for-neural-networks","title":"DataMUX: Data Multiplexing for Neural Networks","date":"2022-02-18","arxiv_id":"2202.09318","repositories_listed":1,"syntology":{"n":9,"n_ran":8,"n_constructed":3,"n_ran_checked":8,"n_instrument":0,"n_unverified":1,"n_honours":2,"n_violates":0,"n_no_contract":6,"n_pointer_only":9,"phrase":"8 ran (of which 3 constructed an object rather than computing a result; 8 with no instrument failure: 2 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/datamux-data-multiplexing-for-neural-networks#ran","syntology_url":"https://syntology.ai/paper/2202.09318","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.09318"}},"official":{"repos":["princeton-nlp/datamux"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":3,"n_ran_no_instrument_failure":8,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/designing-effective-sparse-expert-models","slug":"designing-effective-sparse-expert-models","title":"ST-MoE: Designing Stable and Transferable Sparse Expert Models","date":"2022-02-17","arxiv_id":"2202.08906","repositories_listed":3,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":3,"n_instrument":2,"n_unverified":0,"n_honours":3,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 3 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/designing-effective-sparse-expert-models#ran","syntology_url":"https://syntology.ai/paper/2202.08906","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.08906"}},"official":{"repos":["tensorflow/mesh"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/zerogen-efficient-zero-shot-learning-via","slug":"zerogen-efficient-zero-shot-learning-via","title":"ZeroGen: Efficient Zero-shot Learning via Dataset Generation","date":"2022-02-16","arxiv_id":"2202.07922","repositories_listed":3,"syntology":{"n":7,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":7,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 3 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/zerogen-efficient-zero-shot-learning-via#ran","syntology_url":"https://syntology.ai/paper/2202.07922","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.07922"}},"official":{"repos":["HKUNLP/zerogen"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/data2vec-a-general-framework-for-self-1","slug":"data2vec-a-general-framework-for-self-1","title":"data2vec: A General Framework for Self-supervised Learning in Speech, Vision and Language","date":"2022-02-07","arxiv_id":"2202.03555","repositories_listed":12,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/data2vec-a-general-framework-for-self-1#ran","syntology_url":"https://syntology.ai/paper/2202.03555","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.03555"}},"official":{"repos":["pytorch/fairseq"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/scrolls-standardized-comparison-over-long","slug":"scrolls-standardized-comparison-over-long","title":"SCROLLS: Standardized CompaRison Over Long Language Sequences","date":"2022-01-10","arxiv_id":"2201.03533","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/scrolls-standardized-comparison-over-long#ran","syntology_url":"https://syntology.ai/paper/2201.03533","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.03533"}},"official":{"repos":["tau-nlp/scrolls"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/how-should-pre-trained-language-models-be-1","slug":"how-should-pre-trained-language-models-be-1","title":"How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?","date":"2021-12-22","arxiv_id":"2112.11668","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/how-should-pre-trained-language-models-be-1#ran","syntology_url":"https://syntology.ai/paper/2112.11668","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.11668"}},"official":{"repos":["dongxinshuai/rift-neurips2021"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/summac-re-visiting-nli-based-models-for","slug":"summac-re-visiting-nli-based-models-for","title":"SummaC: Re-Visiting NLI-based Models for Inconsistency Detection in Summarization","date":"2021-11-18","arxiv_id":"2111.09525","repositories_listed":3,"syntology":{"n":23,"n_ran":12,"n_constructed":0,"n_ran_checked":10,"n_instrument":2,"n_unverified":11,"n_honours":1,"n_violates":0,"n_no_contract":9,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 1 honoured, 0 violated, 9 with no contract checked; 2 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/summac-re-visiting-nli-based-models-for#ran","syntology_url":"https://syntology.ai/paper/2111.09525","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.09525"}},"official":{"repos":["tingofurro/summac"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/debertav3-improving-deberta-using-electra","slug":"debertav3-improving-deberta-using-electra","title":"DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing","date":"2021-11-18","arxiv_id":"2111.09543","repositories_listed":3,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/debertav3-improving-deberta-using-electra#ran","syntology_url":"https://syntology.ai/paper/2111.09543","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2111.09543"}},"official":{"repos":["microsoft/DeBERTa"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/metaicl-learning-to-learn-in-context","slug":"metaicl-learning-to-learn-in-context","title":"MetaICL: Learning to Learn In Context","date":"2021-10-29","arxiv_id":"2110.15943","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/metaicl-learning-to-learn-in-context#ran","syntology_url":"https://syntology.ai/paper/2110.15943","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.15943"}},"official":{"repos":["facebookresearch/metaicl"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/can-explanations-be-useful-for-calibrating","slug":"can-explanations-be-useful-for-calibrating","title":"Can Explanations Be Useful for Calibrating Black Box Models?","date":"2021-10-14","arxiv_id":"2110.07586","repositories_listed":2,"syntology":{"n":4,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/can-explanations-be-useful-for-calibrating#ran","syntology_url":"https://syntology.ai/paper/2110.07586","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.07586"}},"official":{"repos":["xiye17/interpcalib"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/contractnli-a-dataset-for-document-level","slug":"contractnli-a-dataset-for-document-level","title":"ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts","date":"2021-10-05","arxiv_id":"2110.01799","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":1,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/contractnli-a-dataset-for-document-level#ran","syntology_url":"https://syntology.ai/paper/2110.01799","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.01799"}},"official":{"repos":["stanfordnlp/contract-nli-bert"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/probing-language-models-for-understanding-of","slug":"probing-language-models-for-understanding-of","title":"Probing Language Models for Understanding of Temporal Expressions","date":"2021-10-03","arxiv_id":"2110.01113","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/probing-language-models-for-understanding-of#ran","syntology_url":"https://syntology.ai/paper/2110.01113","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.01113"}},"official":{"repos":["kunalkukreja21/temporal-expressions-evaluation-lm"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/finding-a-balanced-degree-of-automation-for","slug":"finding-a-balanced-degree-of-automation-for","title":"Finding a Balanced Degree of Automation for Summary Evaluation","date":"2021-09-23","arxiv_id":"2109.11503","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/finding-a-balanced-degree-of-automation-for#ran","syntology_url":"https://syntology.ai/paper/2109.11503","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.11503"}},"official":{"repos":["zhangshiyue/lite2-3pyramid"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/weakly-supervised-explainable-phrasal","slug":"weakly-supervised-explainable-phrasal","title":"Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logic","date":"2021-09-18","arxiv_id":"2109.08927","repositories_listed":1,"syntology":{"n":5,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"phrase":"3 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/weakly-supervised-explainable-phrasal#ran","syntology_url":"https://syntology.ai/paper/2109.08927","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.08927"}},"official":{"repos":["manga-uofa/epr"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/pairwise-supervised-contrastive-learning-of","slug":"pairwise-supervised-contrastive-learning-of","title":"Pairwise Supervised Contrastive Learning of Sentence Representations","date":"2021-09-12","arxiv_id":"2109.05424","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/pairwise-supervised-contrastive-learning-of#ran","syntology_url":"https://syntology.ai/paper/2109.05424","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.05424"}},"official":{"repos":["amazon-research/sentence-representations"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/learning-from-uneven-training-data-unlabeled","slug":"learning-from-uneven-training-data-unlabeled","title":"Learning with Different Amounts of Annotation: From Zero to Many Labels","date":"2021-09-09","arxiv_id":"2109.04408","repositories_listed":1,"syntology":{"n":6,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/learning-from-uneven-training-data-unlabeled#ran","syntology_url":"https://syntology.ai/paper/2109.04408","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.04408"}},"official":{"repos":["szhang42/uneven_training_data"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/spectra-sparse-structured-text","slug":"spectra-sparse-structured-text","title":"SPECTRA: Sparse Structured Text Rationalization","date":"2021-09-09","arxiv_id":"2109.04552","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/spectra-sparse-structured-text#ran","syntology_url":"https://syntology.ai/paper/2109.04552","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.04552"}},"official":{"repos":["deep-spin/spectra-rationalization"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/subword-mapping-and-anchoring-across","slug":"subword-mapping-and-anchoring-across","title":"Subword Mapping and Anchoring across Languages","date":"2021-09-09","arxiv_id":"2109.04556","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/subword-mapping-and-anchoring-across#ran","syntology_url":"https://syntology.ai/paper/2109.04556","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.04556"}},"official":{"repos":["georgevern/smala"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/discrete-and-soft-prompting-for-multilingual","slug":"discrete-and-soft-prompting-for-multilingual","title":"Discrete and Soft Prompting for Multilingual Models","date":"2021-09-08","arxiv_id":"2109.03630","repositories_listed":1,"syntology":{"n":5,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/discrete-and-soft-prompting-for-multilingual#ran","syntology_url":"https://syntology.ai/paper/2109.03630","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.03630"}},"official":{"repos":["mprompting/xlmrprompt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/towards-robustness-against-natural-language-1","slug":"towards-robustness-against-natural-language-1","title":"Towards Robustness Against Natural Language Word Substitutions","date":"2021-07-28","arxiv_id":"2107.13541","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-robustness-against-natural-language-1#ran","syntology_url":"https://syntology.ai/paper/2107.13541","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.13541"}},"official":{"repos":["dongxinshuai/ASCC"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/charformer-fast-character-transformers-via","slug":"charformer-fast-character-transformers-via","title":"Charformer: Fast Character Transformers via Gradient-based Subword Tokenization","date":"2021-06-23","arxiv_id":"2106.12672","repositories_listed":2,"syntology":{"n":10,"n_ran":7,"n_constructed":3,"n_ran_checked":4,"n_instrument":3,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"7 ran (of which 3 constructed an object rather than computing a result; 4 with no instrument failure: 1 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/charformer-fast-character-transformers-via#ran","syntology_url":"https://syntology.ai/paper/2106.12672","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.12672"}},"official":{"repos":["google-research/google-research"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/docnli-a-large-scale-dataset-for-document","slug":"docnli-a-large-scale-dataset-for-document","title":"DocNLI: A Large-scale Dataset for Document-level Natural Language Inference","date":"2021-06-17","arxiv_id":"2106.09449","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/docnli-a-large-scale-dataset-for-document#ran","syntology_url":"https://syntology.ai/paper/2106.09449","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.09449"}},"official":{"repos":["salesforce/DocNLI"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/cblue-a-chinese-biomedical-language","slug":"cblue-a-chinese-biomedical-language","title":"CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark","date":"2021-06-15","arxiv_id":"2106.08087","repositories_listed":2,"syntology":{"n":16,"n_ran":11,"n_constructed":0,"n_ran_checked":8,"n_instrument":3,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":7,"n_pointer_only":2,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 1 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/cblue-a-chinese-biomedical-language#ran","syntology_url":"https://syntology.ai/paper/2106.08087","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.08087"}},"official":{"repos":["cbluebenchmark/cblue"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/variational-information-bottleneck-for-2","slug":"variational-information-bottleneck-for-2","title":"Variational Information Bottleneck for Effective Low-Resource Fine-Tuning","date":"2021-06-10","arxiv_id":"2106.05469","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/variational-information-bottleneck-for-2#ran","syntology_url":"https://syntology.ai/paper/2106.05469","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.05469"}},"official":{"repos":["rabeehk/vibert"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["community"]}}},{"url":"/paper/modeling-hierarchical-structures-with","slug":"modeling-hierarchical-structures-with","title":"Modeling Hierarchical Structures with Continuous Recursive Neural Networks","date":"2021-06-10","arxiv_id":"2106.06038","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":3,"n_pointer_only":0,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 1 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/modeling-hierarchical-structures-with#ran","syntology_url":"https://syntology.ai/paper/2106.06038","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.06038"}},"official":{"repos":["JRC1995/Continuous-RvNN"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/counterfactual-maximum-likelihood-estimation","slug":"counterfactual-maximum-likelihood-estimation","title":"Counterfactual Maximum Likelihood Estimation for Training Deep Networks","date":"2021-06-07","arxiv_id":"2106.03831","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/counterfactual-maximum-likelihood-estimation#ran","syntology_url":"https://syntology.ai/paper/2106.03831","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03831"}},"official":{"repos":["WANGXinyiLinda/CMLE"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/empowering-language-understanding-with","slug":"empowering-language-understanding-with","title":"Empowering Language Understanding with Counterfactual Reasoning","date":"2021-06-06","arxiv_id":"2106.03046","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"1 ran (of which 1 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/empowering-language-understanding-with#ran","syntology_url":"https://syntology.ai/paper/2106.03046","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03046"}},"official":{"repos":["fulifeng/Counterfactual_Reasoning_Model"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/reordering-examples-helps-during-priming","slug":"reordering-examples-helps-during-priming","title":"Reordering Examples Helps during Priming-based Few-Shot Learning","date":"2021-06-03","arxiv_id":"2106.01751","repositories_listed":1,"syntology":{"n":7,"n_ran":3,"n_constructed":2,"n_ran_checked":2,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/reordering-examples-helps-during-priming#ran","syntology_url":"https://syntology.ai/paper/2106.01751","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.01751"}},"official":{"repos":["SawanKumar28/pero"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/neurallog-natural-language-inference-with","slug":"neurallog-natural-language-inference-with","title":"NeuralLog: Natural Language Inference with Joint Neural and Logical Reasoning","date":"2021-05-29","arxiv_id":"2105.14167","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/neurallog-natural-language-inference-with#ran","syntology_url":"https://syntology.ai/paper/2105.14167","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.14167"}},"official":{"repos":["eric11eca/NeuralLog"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/scifive-a-text-to-text-transformer-model-for","slug":"scifive-a-text-to-text-transformer-model-for","title":"SciFive: a text-to-text transformer model for biomedical literature","date":"2021-05-28","arxiv_id":"2106.03598","repositories_listed":1,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":0,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 0 honoured, 0 violated, 5 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/scifive-a-text-to-text-transformer-model-for#ran","syntology_url":"https://syntology.ai/paper/2106.03598","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2106.03598"}},"official":{"repos":["justinphan3110/SciFive"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/ptr-prompt-tuning-with-rules-for-text","slug":"ptr-prompt-tuning-with-rules-for-text","title":"PTR: Prompt Tuning with Rules for Text Classification","date":"2021-05-24","arxiv_id":"2105.11259","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/ptr-prompt-tuning-with-rules-for-text#ran","syntology_url":"https://syntology.ai/paper/2105.11259","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.11259"}},"official":{"repos":["thunlp/PTR"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pay-attention-to-mlps","slug":"pay-attention-to-mlps","title":"Pay Attention to MLPs","date":"2021-05-17","arxiv_id":"2105.08050","repositories_listed":20,"syntology":{"n":44,"n_ran":34,"n_constructed":15,"n_ran_checked":31,"n_instrument":3,"n_unverified":10,"n_honours":1,"n_violates":4,"n_no_contract":26,"n_pointer_only":11,"phrase":"34 ran (of which 15 constructed an object rather than computing a result; 31 with no instrument failure: 1 honoured, 4 violated, 26 with no contract checked; 3 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/pay-attention-to-mlps#ran","syntology_url":"https://syntology.ai/paper/2105.08050","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.08050"}},"official":null}},{"url":"/paper/defsent-sentence-embeddings-using-definition","slug":"defsent-sentence-embeddings-using-definition","title":"DefSent: Sentence Embeddings using Definition Sentences","date":"2021-05-10","arxiv_id":"2105.04339","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/defsent-sentence-embeddings-using-definition#ran","syntology_url":"https://syntology.ai/paper/2105.04339","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.04339"}},"official":{"repos":["hppRC/defsent"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/fnet-mixing-tokens-with-fourier-transforms","slug":"fnet-mixing-tokens-with-fourier-transforms","title":"FNet: Mixing Tokens with Fourier Transforms","date":"2021-05-09","arxiv_id":"2105.03824","repositories_listed":12,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/fnet-mixing-tokens-with-fourier-transforms#ran","syntology_url":"https://syntology.ai/paper/2105.03824","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.03824"}},"official":{"repos":["google-research/google-research"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}},{"url":"/paper/entailment-as-few-shot-learner","slug":"entailment-as-few-shot-learner","title":"Entailment as Few-Shot Learner","date":"2021-04-29","arxiv_id":"2104.14690","repositories_listed":3,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/entailment-as-few-shot-learner#ran","syntology_url":"https://syntology.ai/paper/2104.14690","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.14690"}},"official":null}}],"record_sha256":"e19d22071cd4dd640a6e247f6d1c4ad32347182cbe715f35e4dd15a6b7298150","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}