{"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/language-modelling/papers/9","list_of":"/task/language-modelling","task":"Language Modelling","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":"archive","order_definition":"repositories listed in the archive (most first), then date (newest first), then slug","page":9,"pages_in_order":177,"rows_per_page":100,"rows":[801,900],"of":17610,"counts":{"archive_papers_tagged":17610,"with_a_code_link":7012,"where_syntology_ran_a_sample":2428,"not_listed_spam_title":0,"listed":17610,"listed_where_code_ran":2428,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":2027,"every_run_a_failure_of_syntologys_instrument":401,"listed_with_a_run_with_no_instrument_failure":2027,"listed_every_run_a_failure_of_syntologys_instrument":401,"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/language-modelling","prev":"/task/language-modelling/papers/8","next":"/task/language-modelling/papers/10","papers":[{"url":"/paper/identifying-interpretable-subspaces-in-image","slug":"identifying-interpretable-subspaces-in-image","title":"Identifying Interpretable Subspaces in Image Representations","date":"2023-07-20","arxiv_id":"2307.10504","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/identifying-interpretable-subspaces-in-image#ran","syntology_url":"https://syntology.ai/paper/2307.10504","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.10504"}},"official":{"repos":["nehakalibhat/falcon-explain"],"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/multimodal-machine-learning-for-extraction-of","slug":"multimodal-machine-learning-for-extraction-of","title":"Modular Multimodal Machine Learning for Extraction of Theorems and Proofs in Long Scientific Documents (Extended Version)","date":"2023-07-18","arxiv_id":"2307.09047","repositories_listed":2,"syntology":null},{"url":"/paper/generating-benchmarks-for-factuality","slug":"generating-benchmarks-for-factuality","title":"Generating Benchmarks for Factuality Evaluation of Language Models","date":"2023-07-13","arxiv_id":"2307.06908","repositories_listed":2,"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/generating-benchmarks-for-factuality#ran","syntology_url":"https://syntology.ai/paper/2307.06908","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.06908"}},"official":{"repos":["ai21labs/factor"],"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/in-context-autoencoder-for-context","slug":"in-context-autoencoder-for-context","title":"In-context Autoencoder for Context Compression in a Large Language Model","date":"2023-07-13","arxiv_id":"2307.06945","repositories_listed":2,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":3,"n_instrument":3,"n_unverified":0,"n_honours":0,"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; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 3 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/in-context-autoencoder-for-context#ran","syntology_url":"https://syntology.ai/paper/2307.06945","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.06945"}},"official":{"repos":["getao/icae"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/what-matters-in-training-a-gpt4-style","slug":"what-matters-in-training-a-gpt4-style","title":"What Matters in Training a GPT4-Style Language Model with Multimodal Inputs?","date":"2023-07-05","arxiv_id":"2307.02469","repositories_listed":2,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":3,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":2,"n_pointer_only":1,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/what-matters-in-training-a-gpt4-style#ran","syntology_url":"https://syntology.ai/paper/2307.02469","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.02469"}},"official":null}},{"url":"/paper/biocpt-contrastive-pre-trained-transformers","slug":"biocpt-contrastive-pre-trained-transformers","title":"MedCPT: Contrastive Pre-trained Transformers with Large-scale PubMed Search Logs for Zero-shot Biomedical Information Retrieval","date":"2023-07-02","arxiv_id":"2307.00589","repositories_listed":2,"syntology":null},{"url":"/paper/composing-parameter-efficient-modules-with","slug":"composing-parameter-efficient-modules-with","title":"Composing Parameter-Efficient Modules with Arithmetic Operations","date":"2023-06-26","arxiv_id":"2306.14870","repositories_listed":2,"syntology":{"n":10,"n_ran":5,"n_constructed":0,"n_ran_checked":5,"n_instrument":0,"n_unverified":5,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":4,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/composing-parameter-efficient-modules-with#ran","syntology_url":"https://syntology.ai/paper/2306.14870","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.14870"}},"official":{"repos":["hkust-nlp/pem_composition","sjtu-lit/pem_composition"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/kosmos-2-grounding-multimodal-large-language","slug":"kosmos-2-grounding-multimodal-large-language","title":"Kosmos-2: Grounding Multimodal Large Language Models to the World","date":"2023-06-26","arxiv_id":"2306.14824","repositories_listed":2,"syntology":null},{"url":"/paper/conformal-language-modeling","slug":"conformal-language-modeling","title":"Conformal Language Modeling","date":"2023-06-16","arxiv_id":"2306.10193","repositories_listed":2,"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":1,"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/conformal-language-modeling#ran","syntology_url":"https://syntology.ai/paper/2306.10193","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.10193"}},"official":{"repos":["varal7/conformal-language-modeling"],"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/scale-scaling-up-the-complexity-for-advanced","slug":"scale-scaling-up-the-complexity-for-advanced","title":"One Law, Many Languages: Benchmarking Multilingual Legal Reasoning for Judicial Support","date":"2023-06-15","arxiv_id":"2306.09237","repositories_listed":2,"syntology":null},{"url":"/paper/webglm-towards-an-efficient-web-enhanced","slug":"webglm-towards-an-efficient-web-enhanced","title":"WebGLM: Towards An Efficient Web-Enhanced Question Answering System with Human Preferences","date":"2023-06-13","arxiv_id":"2306.07906","repositories_listed":2,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/webglm-towards-an-efficient-web-enhanced#ran","syntology_url":"https://syntology.ai/paper/2306.07906","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.07906"}},"official":{"repos":["thudm/webglm"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/waffling-around-for-performance-visual","slug":"waffling-around-for-performance-visual","title":"Waffling around for Performance: Visual Classification with Random Words and Broad Concepts","date":"2023-06-12","arxiv_id":"2306.07282","repositories_listed":2,"syntology":{"n":5,"n_ran":5,"n_constructed":0,"n_ran_checked":0,"n_instrument":5,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"5 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; 5 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/waffling-around-for-performance-visual#ran","syntology_url":"https://syntology.ai/paper/2306.07282","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.07282"}},"official":{"repos":["explainableml/waffleclip"],"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":["listed","official"]}}},{"url":"/paper/14-examples-of-how-llms-can-transform","slug":"14-examples-of-how-llms-can-transform","title":"14 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathon","date":"2023-06-09","arxiv_id":"2306.06283","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/14-examples-of-how-llms-can-transform#ran","syntology_url":"https://syntology.ai/paper/2306.06283","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.06283"}},"official":{"repos":["qai222/llm_organic_synthesis","doncamilom/bollama"],"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/fingpt-open-source-financial-large-language","slug":"fingpt-open-source-financial-large-language","title":"FinGPT: Open-Source Financial Large Language Models","date":"2023-06-09","arxiv_id":"2306.06031","repositories_listed":2,"syntology":{"n":11,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":5,"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) · 5 unverified","sample_list":"/paper/fingpt-open-source-financial-large-language#ran","syntology_url":"https://syntology.ai/paper/2306.06031","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.06031"}},"official":{"repos":["ai4finance-foundation/fingpt","ai4finance-foundation/finnlp"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/pandalm-an-automatic-evaluation-benchmark-for","slug":"pandalm-an-automatic-evaluation-benchmark-for","title":"PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization","date":"2023-06-08","arxiv_id":"2306.05087","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"3 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pandalm-an-automatic-evaluation-benchmark-for#ran","syntology_url":"https://syntology.ai/paper/2306.05087","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05087"}},"official":{"repos":["weopenml/pandalm"],"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":["listed","official"]}}},{"url":"/paper/pixiu-a-large-language-model-instruction-data","slug":"pixiu-a-large-language-model-instruction-data","title":"PIXIU: A Large Language Model, Instruction Data and Evaluation Benchmark for Finance","date":"2023-06-08","arxiv_id":"2306.05443","repositories_listed":2,"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/pixiu-a-large-language-model-instruction-data#ran","syntology_url":"https://syntology.ai/paper/2306.05443","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.05443"}},"official":{"repos":["chancefocus/pixiu"],"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/llmzip-lossless-text-compression-using-large","slug":"llmzip-lossless-text-compression-using-large","title":"LLMZip: Lossless Text Compression using Large Language Models","date":"2023-06-06","arxiv_id":"2306.04050","repositories_listed":2,"syntology":null},{"url":"/paper/comet-learning-cardinality-constrained","slug":"comet-learning-cardinality-constrained","title":"COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search","date":"2023-06-05","arxiv_id":"2306.02824","repositories_listed":2,"syntology":null},{"url":"/paper/sequential-monte-carlo-steering-of-large","slug":"sequential-monte-carlo-steering-of-large","title":"Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs","date":"2023-06-05","arxiv_id":"2306.03081","repositories_listed":2,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":4,"n_instrument":2,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/sequential-monte-carlo-steering-of-large#ran","syntology_url":"https://syntology.ai/paper/2306.03081","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.03081"}},"official":{"repos":["probcomp/hfppl","probcomp/llamppl"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/enhancing-the-protein-tertiary-structure","slug":"enhancing-the-protein-tertiary-structure","title":"Enhancing the Protein Tertiary Structure Prediction by Multiple Sequence Alignment Generation","date":"2023-06-02","arxiv_id":"2306.01824","repositories_listed":2,"syntology":null},{"url":"/paper/kl-divergence-guided-temperature-sampling","slug":"kl-divergence-guided-temperature-sampling","title":"KL-Divergence Guided Temperature Sampling","date":"2023-06-02","arxiv_id":"2306.01286","repositories_listed":2,"syntology":null},{"url":"/paper/faster-causal-attention-over-large-sequences","slug":"faster-causal-attention-over-large-sequences","title":"Faster Causal Attention Over Large Sequences Through Sparse Flash Attention","date":"2023-06-01","arxiv_id":"2306.01160","repositories_listed":2,"syntology":null},{"url":"/paper/preference-grounded-token-level-guidance-for-1","slug":"preference-grounded-token-level-guidance-for-1","title":"Preference-grounded Token-level Guidance for Language Model Fine-tuning","date":"2023-06-01","arxiv_id":"2306.00398","repositories_listed":2,"syntology":{"n":18,"n_ran":8,"n_constructed":1,"n_ran_checked":2,"n_instrument":6,"n_unverified":10,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"phrase":"8 ran (of which 1 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 6 where Syntology's instrument failed) · 10 unverified","sample_list":"/paper/preference-grounded-token-level-guidance-for-1#ran","syntology_url":"https://syntology.ai/paper/2306.00398","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2306.00398"}},"official":{"repos":["shentao-yang/preference_grounded_guidance"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/mert-acoustic-music-understanding-model-with","slug":"mert-acoustic-music-understanding-model-with","title":"MERT: Acoustic Music Understanding Model with Large-Scale Self-supervised Training","date":"2023-05-31","arxiv_id":"2306.00107","repositories_listed":2,"syntology":null},{"url":"/paper/red-teaming-language-model-detectors-with","slug":"red-teaming-language-model-detectors-with","title":"Red Teaming Language Model Detectors with Language Models","date":"2023-05-31","arxiv_id":"2305.19713","repositories_listed":2,"syntology":{"n":18,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":7,"n_honours":0,"n_violates":1,"n_no_contract":10,"n_pointer_only":1,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 1 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/red-teaming-language-model-detectors-with#ran","syntology_url":"https://syntology.ai/paper/2305.19713","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19713"}},"official":{"repos":["shizhouxing/Attack-LM-Detectors"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":3,"ran_from_kinds":["named_in_paper","official"]}}},{"url":"/paper/structure-aware-language-model-pretraining","slug":"structure-aware-language-model-pretraining","title":"Structure-Aware Language Model Pretraining Improves Dense Retrieval on Structured Data","date":"2023-05-31","arxiv_id":"2305.19912","repositories_listed":2,"syntology":{"n":8,"n_ran":8,"n_constructed":0,"n_ran_checked":8,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":4,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 1 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/structure-aware-language-model-pretraining#ran","syntology_url":"https://syntology.ai/paper/2305.19912","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19912"}},"official":{"repos":["openmatch/openmatch"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/lance-stress-testing-visual-models-by-1","slug":"lance-stress-testing-visual-models-by-1","title":"LANCE: Stress-testing Visual Models by Generating Language-guided Counterfactual Images","date":"2023-05-30","arxiv_id":"2305.19164","repositories_listed":2,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":4,"phrase":"10 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/lance-stress-testing-visual-models-by-1#ran","syntology_url":"https://syntology.ai/paper/2305.19164","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.19164"}},"official":{"repos":["virajprabhu/lance"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":8,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/pali-x-on-scaling-up-a-multilingual-vision","slug":"pali-x-on-scaling-up-a-multilingual-vision","title":"PaLI-X: On Scaling up a Multilingual Vision and Language Model","date":"2023-05-29","arxiv_id":"2305.18565","repositories_listed":2,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":4,"n_no_contract":1,"n_pointer_only":2,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 1 honoured, 4 violated, 1 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pali-x-on-scaling-up-a-multilingual-vision#ran","syntology_url":"https://syntology.ai/paper/2305.18565","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18565"}},"official":null}},{"url":"/paper/vast-a-vision-audio-subtitle-text-omni-1","slug":"vast-a-vision-audio-subtitle-text-omni-1","title":"VAST: A Vision-Audio-Subtitle-Text Omni-Modality Foundation Model and Dataset","date":"2023-05-29","arxiv_id":"2305.18500","repositories_listed":2,"syntology":{"n":42,"n_ran":35,"n_constructed":4,"n_ran_checked":29,"n_instrument":6,"n_unverified":7,"n_honours":2,"n_violates":1,"n_no_contract":26,"n_pointer_only":8,"phrase":"35 ran (of which 4 constructed an object rather than computing a result; 29 with no instrument failure: 2 honoured, 1 violated, 26 with no contract checked; 6 where Syntology's instrument failed) · 7 unverified","sample_list":"/paper/vast-a-vision-audio-subtitle-text-omni-1#ran","syntology_url":"https://syntology.ai/paper/2305.18500","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.18500"}},"official":{"repos":["txh-mercury/vast"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":4,"n_ran_no_instrument_failure":12,"n_unverified":7,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/language-models-can-improve-event-prediction-1","slug":"language-models-can-improve-event-prediction-1","title":"Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning","date":"2023-05-26","arxiv_id":"2305.16646","repositories_listed":2,"syntology":{"n":24,"n_ran":19,"n_constructed":4,"n_ran_checked":17,"n_instrument":2,"n_unverified":5,"n_honours":0,"n_violates":1,"n_no_contract":16,"n_pointer_only":4,"phrase":"19 ran (of which 4 constructed an object rather than computing a result; 17 with no instrument failure: 0 honoured, 1 violated, 16 with no contract checked; 2 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/language-models-can-improve-event-prediction-1#ran","syntology_url":"https://syntology.ai/paper/2305.16646","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.16646"}},"official":{"repos":["ant-research/easytemporalpointprocess","ilampard/lamp"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":4,"n_ran_no_instrument_failure":12,"n_unverified":3,"ran_from_kinds":["found_in_text","official"]}}},{"url":"/paper/expertprompting-instructing-large-language","slug":"expertprompting-instructing-large-language","title":"ExpertPrompting: Instructing Large Language Models to be Distinguished Experts","date":"2023-05-24","arxiv_id":"2305.14688","repositories_listed":2,"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":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) · 0 unverified","sample_list":"/paper/expertprompting-instructing-large-language#ran","syntology_url":"https://syntology.ai/paper/2305.14688","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.14688"}},"official":{"repos":["ofa-sys/expertllama"],"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/huatuogpt-towards-taming-language-model-to-be","slug":"huatuogpt-towards-taming-language-model-to-be","title":"HuatuoGPT, towards Taming Language Model to Be a Doctor","date":"2023-05-24","arxiv_id":"2305.15075","repositories_listed":2,"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/huatuogpt-towards-taming-language-model-to-be#ran","syntology_url":"https://syntology.ai/paper/2305.15075","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.15075"}},"official":{"repos":["freedomintelligence/huatuogpt"],"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/mquake-assessing-knowledge-editing-in","slug":"mquake-assessing-knowledge-editing-in","title":"MQuAKE: Assessing Knowledge Editing in Language Models via Multi-Hop Questions","date":"2023-05-24","arxiv_id":"2305.14795","repositories_listed":2,"syntology":null},{"url":"/paper/unit-based-speech-to-speech-translation","slug":"unit-based-speech-to-speech-translation","title":"Textless Speech-to-Speech Translation With Limited Parallel Data","date":"2023-05-24","arxiv_id":"2305.15405","repositories_listed":2,"syntology":null},{"url":"/paper/focus-effective-embedding-initialization-for","slug":"focus-effective-embedding-initialization-for","title":"FOCUS: Effective Embedding Initialization for Monolingual Specialization of Multilingual Models","date":"2023-05-23","arxiv_id":"2305.14481","repositories_listed":2,"syntology":null},{"url":"/paper/llm-grounded-diffusion-enhancing-prompt","slug":"llm-grounded-diffusion-enhancing-prompt","title":"LLM-grounded Diffusion: Enhancing Prompt Understanding of Text-to-Image Diffusion Models with Large Language Models","date":"2023-05-23","arxiv_id":"2305.13655","repositories_listed":2,"syntology":null},{"url":"/paper/reticl-sequential-retrieval-of-in-context","slug":"reticl-sequential-retrieval-of-in-context","title":"RetICL: Sequential Retrieval of In-Context Examples with Reinforcement Learning","date":"2023-05-23","arxiv_id":"2305.14502","repositories_listed":2,"syntology":null},{"url":"/paper/recurrentgpt-interactive-generation-of","slug":"recurrentgpt-interactive-generation-of","title":"RecurrentGPT: Interactive Generation of (Arbitrarily) Long Text","date":"2023-05-22","arxiv_id":"2305.13304","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":2,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 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; 1 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/recurrentgpt-interactive-generation-of#ran","syntology_url":"https://syntology.ai/paper/2305.13304","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.13304"}},"official":{"repos":["aiwaves-cn/recurrentgpt"],"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-distilled-student-models-co-training","slug":"disco-distilled-student-models-co-training","title":"DisCo: Distilled Student Models Co-training for Semi-supervised Text Mining","date":"2023-05-20","arxiv_id":"2305.12074","repositories_listed":2,"syntology":{"n":17,"n_ran":5,"n_constructed":1,"n_ran_checked":5,"n_instrument":0,"n_unverified":12,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":17,"phrase":"5 ran (of which 1 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) · 12 unverified","sample_list":"/paper/disco-distilled-student-models-co-training#ran","syntology_url":"https://syntology.ai/paper/2305.12074","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.12074"}},"official":{"repos":["litesslhub/disco"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":11,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/clinical-camel-an-open-source-expert-level","slug":"clinical-camel-an-open-source-expert-level","title":"Clinical Camel: An Open Expert-Level Medical Language Model with Dialogue-Based Knowledge Encoding","date":"2023-05-19","arxiv_id":"2305.12031","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"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) · 0 unverified","sample_list":"/paper/clinical-camel-an-open-source-expert-level#ran","syntology_url":"https://syntology.ai/paper/2305.12031","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.12031"}},"official":{"repos":["bowang-lab/clinical-camel"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/syllable-discovery-and-cross-lingual","slug":"syllable-discovery-and-cross-lingual","title":"Syllable Discovery and Cross-Lingual Generalization in a Visually Grounded, Self-Supervised Speech Model","date":"2023-05-19","arxiv_id":"2305.11435","repositories_listed":2,"syntology":{"n":16,"n_ran":14,"n_constructed":0,"n_ran_checked":12,"n_instrument":2,"n_unverified":2,"n_honours":2,"n_violates":0,"n_no_contract":10,"n_pointer_only":1,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 2 honoured, 0 violated, 10 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/syllable-discovery-and-cross-lingual#ran","syntology_url":"https://syntology.ai/paper/2305.11435","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.11435"}},"official":{"repos":["jasonppy/syllable-discovery"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/visionllm-large-language-model-is-also-an","slug":"visionllm-large-language-model-is-also-an","title":"VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks","date":"2023-05-18","arxiv_id":"2305.11175","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/visionllm-large-language-model-is-also-an#ran","syntology_url":"https://syntology.ai/paper/2305.11175","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.11175"}},"official":{"repos":["opengvlab/visionllm","opengvlab/interngpt"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/a-better-way-to-do-masked-language-model","slug":"a-better-way-to-do-masked-language-model","title":"A Better Way to Do Masked Language Model Scoring","date":"2023-05-17","arxiv_id":"2305.10588","repositories_listed":2,"syntology":{"n":5,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 4 unverified","sample_list":"/paper/a-better-way-to-do-masked-language-model#ran","syntology_url":"https://syntology.ai/paper/2305.10588","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.10588"}},"official":{"repos":["carina-kauf/better-mlm-scoring"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["listed"]}}},{"url":"/paper/doremi-optimizing-data-mixtures-speeds-up-1","slug":"doremi-optimizing-data-mixtures-speeds-up-1","title":"DoReMi: Optimizing Data Mixtures Speeds Up Language Model Pretraining","date":"2023-05-17","arxiv_id":"2305.10429","repositories_listed":2,"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/doremi-optimizing-data-mixtures-speeds-up-1#ran","syntology_url":"https://syntology.ai/paper/2305.10429","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.10429"}},"official":{"repos":["sangmichaelxie/doremi"],"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/pmc-vqa-visual-instruction-tuning-for-medical","slug":"pmc-vqa-visual-instruction-tuning-for-medical","title":"PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering","date":"2023-05-17","arxiv_id":"2305.10415","repositories_listed":2,"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":2,"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/pmc-vqa-visual-instruction-tuning-for-medical#ran","syntology_url":"https://syntology.ai/paper/2305.10415","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.10415"}},"official":{"repos":["xiaoman-zhang/PMC-VQA"],"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/mpi-rical-data-driven-mpi-distributed","slug":"mpi-rical-data-driven-mpi-distributed","title":"MPI-rical: Data-Driven MPI Distributed Parallelism Assistance with Transformers","date":"2023-05-16","arxiv_id":"2305.09438","repositories_listed":2,"syntology":null},{"url":"/paper/mobile-env-a-universal-platform-for-training","slug":"mobile-env-a-universal-platform-for-training","title":"Mobile-Env: Building Qualified Evaluation Benchmarks for LLM-GUI Interaction","date":"2023-05-14","arxiv_id":"2305.08144","repositories_listed":2,"syntology":{"n":17,"n_ran":11,"n_constructed":0,"n_ran_checked":11,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"11 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/mobile-env-a-universal-platform-for-training#ran","syntology_url":"https://syntology.ai/paper/2305.08144","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.08144"}},"official":{"repos":["opendfm/mobile-env-expe","x-lance/mobile-env"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/investigating-emergent-goal-like-behaviour-in","slug":"investigating-emergent-goal-like-behaviour-in","title":"The Machine Psychology of Cooperation: Can GPT models operationalise prompts for altruism, cooperation, competitiveness and selfishness in economic games?","date":"2023-05-13","arxiv_id":"2305.07970","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/investigating-emergent-goal-like-behaviour-in#ran","syntology_url":"https://syntology.ai/paper/2305.07970","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.07970"}},"official":{"repos":["phelps-sg/llm-cooperation","gitlab.com/sphelps/llm-cooperation"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/internchat-solving-vision-centric-tasks-by","slug":"internchat-solving-vision-centric-tasks-by","title":"InternGPT: Solving Vision-Centric Tasks by Interacting with ChatGPT Beyond Language","date":"2023-05-09","arxiv_id":"2305.05662","repositories_listed":2,"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/internchat-solving-vision-centric-tasks-by#ran","syntology_url":"https://syntology.ai/paper/2305.05662","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.05662"}},"official":{"repos":["opengvlab/internchat","opengvlab/interngpt"],"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/promptrank-unsupervised-keyphrase-extraction","slug":"promptrank-unsupervised-keyphrase-extraction","title":"PromptRank: Unsupervised Keyphrase Extraction Using Prompt","date":"2023-05-08","arxiv_id":"2305.04490","repositories_listed":2,"syntology":null},{"url":"/paper/toeplitz-neural-network-for-sequence-modeling","slug":"toeplitz-neural-network-for-sequence-modeling","title":"Toeplitz Neural Network for Sequence Modeling","date":"2023-05-08","arxiv_id":"2305.04749","repositories_listed":2,"syntology":{"n":6,"n_ran":2,"n_constructed":1,"n_ran_checked":1,"n_instrument":1,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":0,"phrase":"2 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; 1 where Syntology's instrument failed) · 4 unverified","sample_list":"/paper/toeplitz-neural-network-for-sequence-modeling#ran","syntology_url":"https://syntology.ai/paper/2305.04749","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.04749"}},"official":{"repos":["Doraemonzzz/tnn-pytorch"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/x-llm-bootstrapping-advanced-large-language","slug":"x-llm-bootstrapping-advanced-large-language","title":"X-LLM: Bootstrapping Advanced Large Language Models by Treating Multi-Modalities as Foreign Languages","date":"2023-05-07","arxiv_id":"2305.04160","repositories_listed":2,"syntology":{"n":11,"n_ran":8,"n_constructed":0,"n_ran_checked":4,"n_instrument":4,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":2,"phrase":"8 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; 4 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/x-llm-bootstrapping-advanced-large-language#ran","syntology_url":"https://syntology.ai/paper/2305.04160","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.04160"}},"official":null}},{"url":"/paper/mindgames-targeting-theory-of-mind-in-large","slug":"mindgames-targeting-theory-of-mind-in-large","title":"MindGames: Targeting Theory of Mind in Large Language Models with Dynamic Epistemic Modal Logic","date":"2023-05-05","arxiv_id":"2305.03353","repositories_listed":2,"syntology":null},{"url":"/paper/cognitive-reframing-of-negative-thoughts","slug":"cognitive-reframing-of-negative-thoughts","title":"Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction","date":"2023-05-04","arxiv_id":"2305.02466","repositories_listed":2,"syntology":null},{"url":"/paper/codegen2-lessons-for-training-llms-on","slug":"codegen2-lessons-for-training-llms-on","title":"CodeGen2: Lessons for Training LLMs on Programming and Natural Languages","date":"2023-05-03","arxiv_id":"2305.02309","repositories_listed":2,"syntology":null},{"url":"/paper/how-to-unleash-the-power-of-large-language","slug":"how-to-unleash-the-power-of-large-language","title":"How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?","date":"2023-05-02","arxiv_id":"2305.01555","repositories_listed":2,"syntology":null},{"url":"/paper/assessing-working-memory-capacity-of-chatgpt","slug":"assessing-working-memory-capacity-of-chatgpt","title":"Working Memory Capacity of ChatGPT: An Empirical Study","date":"2023-04-30","arxiv_id":"2305.03731","repositories_listed":2,"syntology":null},{"url":"/paper/is-a-prompt-and-a-few-samples-all-you-need","slug":"is-a-prompt-and-a-few-samples-all-you-need","title":"The Parrot Dilemma: Human-Labeled vs. LLM-augmented Data in Classification Tasks","date":"2023-04-26","arxiv_id":"2304.13861","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":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/is-a-prompt-and-a-few-samples-all-you-need#ran","syntology_url":"https://syntology.ai/paper/2304.13861","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.13861"}},"official":{"repos":["andersgiovanni/worker_vs_gpt","AGMoller/worker_vs_gpt"],"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/codekgc-code-language-model-for-generative","slug":"codekgc-code-language-model-for-generative","title":"CodeKGC: Code Language Model for Generative Knowledge Graph Construction","date":"2023-04-18","arxiv_id":"2304.09048","repositories_listed":2,"syntology":{"n":16,"n_ran":14,"n_constructed":0,"n_ran_checked":13,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":13,"n_pointer_only":1,"phrase":"14 ran (of which 0 constructed an object rather than computing a result; 13 with no instrument failure: 0 honoured, 0 violated, 13 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/codekgc-code-language-model-for-generative#ran","syntology_url":"https://syntology.ai/paper/2304.09048","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.09048"}},"official":{"repos":["zjunlp/deepke"],"state":"official (archive's flag): 14 ran","n_ran":14,"n_constructed":0,"n_ran_no_instrument_failure":13,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/api-bank-a-benchmark-for-tool-augmented-llms","slug":"api-bank-a-benchmark-for-tool-augmented-llms","title":"API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs","date":"2023-04-14","arxiv_id":"2304.08244","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":2,"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 2 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; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/api-bank-a-benchmark-for-tool-augmented-llms#ran","syntology_url":"https://syntology.ai/paper/2304.08244","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.08244"}},"official":null}},{"url":"/paper/clip-surgery-for-better-explainability-with","slug":"clip-surgery-for-better-explainability-with","title":"A Closer Look at the Explainability of Contrastive Language-Image Pre-training","date":"2023-04-12","arxiv_id":"2304.05653","repositories_listed":2,"syntology":null},{"url":"/paper/teaching-large-language-models-to-self-debug","slug":"teaching-large-language-models-to-self-debug","title":"Teaching Large Language Models to Self-Debug","date":"2023-04-11","arxiv_id":"2304.05128","repositories_listed":2,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"0 ran · 1 unverified","sample_list":"/paper/teaching-large-language-models-to-self-debug#ran","syntology_url":"https://syntology.ai/paper/2304.05128","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.05128"}},"official":null}},{"url":"/paper/inference-with-reference-lossless","slug":"inference-with-reference-lossless","title":"Inference with Reference: Lossless Acceleration of Large Language Models","date":"2023-04-10","arxiv_id":"2304.04487","repositories_listed":2,"syntology":null},{"url":"/paper/crowdclip-unsupervised-crowd-counting-via","slug":"crowdclip-unsupervised-crowd-counting-via","title":"CrowdCLIP: Unsupervised Crowd Counting via Vision-Language Model","date":"2023-04-09","arxiv_id":"2304.04231","repositories_listed":2,"syntology":{"n":1,"n_ran":0,"n_constructed":0,"n_ran_checked":0,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"phrase":"0 ran · 1 unverified","sample_list":"/paper/crowdclip-unsupervised-crowd-counting-via#ran","syntology_url":"https://syntology.ai/paper/2304.04231","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.04231"}},"official":{"repos":["dk-liang/crowdclip"],"state":"official: harvested, nothing ran","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":[]}}},{"url":"/paper/llm-adapters-an-adapter-family-for-parameter","slug":"llm-adapters-an-adapter-family-for-parameter","title":"LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models","date":"2023-04-04","arxiv_id":"2304.01933","repositories_listed":2,"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":1,"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/llm-adapters-an-adapter-family-for-parameter#ran","syntology_url":"https://syntology.ai/paper/2304.01933","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.01933"}},"official":{"repos":["agi-edgerunners/llm-adapters"],"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/pk-chat-pointer-network-guided-knowledge","slug":"pk-chat-pointer-network-guided-knowledge","title":"PK-Chat: Pointer Network Guided Knowledge Driven Generative Dialogue Model","date":"2023-04-02","arxiv_id":"2304.00592","repositories_listed":2,"syntology":null},{"url":"/paper/bloomberggpt-a-large-language-model-for","slug":"bloomberggpt-a-large-language-model-for","title":"BloombergGPT: A Large Language Model for Finance","date":"2023-03-30","arxiv_id":"2303.17564","repositories_listed":2,"syntology":null},{"url":"/paper/vila-learning-image-aesthetics-from-user","slug":"vila-learning-image-aesthetics-from-user","title":"VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining","date":"2023-03-24","arxiv_id":"2303.14302","repositories_listed":2,"syntology":null},{"url":"/paper/neural-implicit-vision-language-feature","slug":"neural-implicit-vision-language-feature","title":"Neural Implicit Vision-Language Feature Fields","date":"2023-03-20","arxiv_id":"2303.10962","repositories_listed":2,"syntology":null},{"url":"/paper/bangla-grammatical-error-detection-using-t5","slug":"bangla-grammatical-error-detection-using-t5","title":"Bangla Grammatical Error Detection Using T5 Transformer Model","date":"2023-03-19","arxiv_id":"2303.10612","repositories_listed":2,"syntology":null},{"url":"/paper/trained-on-100-million-words-and-still-in","slug":"trained-on-100-million-words-and-still-in","title":"Trained on 100 million words and still in shape: BERT meets British National Corpus","date":"2023-03-17","arxiv_id":"2303.09859","repositories_listed":2,"syntology":{"n":7,"n_ran":3,"n_constructed":3,"n_ran_checked":3,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":7,"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) · 4 unverified; every one of the 3 samples that ran constructed an object rather than computing a result","sample_list":"/paper/trained-on-100-million-words-and-still-in#ran","syntology_url":"https://syntology.ai/paper/2303.09859","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.09859"}},"official":{"repos":["ltgoslo/ltg-bert"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/jump-to-conclusions-short-cutting","slug":"jump-to-conclusions-short-cutting","title":"Jump to Conclusions: Short-Cutting Transformers With Linear Transformations","date":"2023-03-16","arxiv_id":"2303.09435","repositories_listed":2,"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/jump-to-conclusions-short-cutting#ran","syntology_url":"https://syntology.ai/paper/2303.09435","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.09435"}},"official":{"repos":["sashayd/mat"],"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/rethinking-model-ensemble-in-transfer-based","slug":"rethinking-model-ensemble-in-transfer-based","title":"Rethinking Model Ensemble in Transfer-based Adversarial Attacks","date":"2023-03-16","arxiv_id":"2303.09105","repositories_listed":2,"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/rethinking-model-ensemble-in-transfer-based#ran","syntology_url":"https://syntology.ai/paper/2303.09105","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.09105"}},"official":{"repos":["huanranchen/AdversarialAttacks"],"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/eliciting-latent-predictions-from","slug":"eliciting-latent-predictions-from","title":"Eliciting Latent Predictions from Transformers with the Tuned Lens","date":"2023-03-14","arxiv_id":"2303.08112","repositories_listed":2,"syntology":{"n":15,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":1,"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) · 3 unverified","sample_list":"/paper/eliciting-latent-predictions-from#ran","syntology_url":"https://syntology.ai/paper/2303.08112","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.08112"}},"official":{"repos":["alignmentresearch/tuned-lens","neelnanda-io/transformerlens"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/evaluation-of-chatgpt-as-a-question-answering","slug":"evaluation-of-chatgpt-as-a-question-answering","title":"Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM Family","date":"2023-03-14","arxiv_id":"2303.07992","repositories_listed":2,"syntology":null},{"url":"/paper/tag2text-guiding-vision-language-model-via","slug":"tag2text-guiding-vision-language-model-via","title":"Tag2Text: Guiding Vision-Language Model via Image Tagging","date":"2023-03-10","arxiv_id":"2303.05657","repositories_listed":2,"syntology":null},{"url":"/paper/preparing-the-vuk-uzenzele-and-za-gov","slug":"preparing-the-vuk-uzenzele-and-za-gov","title":"Preparing the Vuk'uzenzele and ZA-gov-multilingual South African multilingual corpora","date":"2023-03-07","arxiv_id":"2303.03750","repositories_listed":2,"syntology":{"n":17,"n_ran":12,"n_constructed":0,"n_ran_checked":11,"n_instrument":1,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 1 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/preparing-the-vuk-uzenzele-and-za-gov#ran","syntology_url":"https://syntology.ai/paper/2303.03750","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.03750"}},"official":{"repos":["dsfsi/gov-za-multilingual","dsfsi/vukuzenzele-nlp"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":11,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/a-multi-grained-self-interpretable-symbolic","slug":"a-multi-grained-self-interpretable-symbolic","title":"A Multi-Grained Self-Interpretable Symbolic-Neural Model For Single/Multi-Labeled Text Classification","date":"2023-03-06","arxiv_id":"2303.02860","repositories_listed":2,"syntology":null},{"url":"/paper/palm-e-an-embodied-multimodal-language-model","slug":"palm-e-an-embodied-multimodal-language-model","title":"PaLM-E: An Embodied Multimodal Language Model","date":"2023-03-06","arxiv_id":"2303.03378","repositories_listed":2,"syntology":null},{"url":"/paper/prismer-a-vision-language-model-with-an","slug":"prismer-a-vision-language-model-with-an","title":"Prismer: A Vision-Language Model with Multi-Task Experts","date":"2023-03-04","arxiv_id":"2303.02506","repositories_listed":2,"syntology":null},{"url":"/paper/automatic-prompt-augmentation-and-selection","slug":"automatic-prompt-augmentation-and-selection","title":"Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data","date":"2023-02-24","arxiv_id":"2302.12822","repositories_listed":2,"syntology":null},{"url":"/paper/federated-learning-for-asr-based-on-wav2vec-2","slug":"federated-learning-for-asr-based-on-wav2vec-2","title":"Federated Learning for ASR based on Wav2vec 2.0","date":"2023-02-20","arxiv_id":"2302.10790","repositories_listed":2,"syntology":null},{"url":"/paper/towards-universal-fake-image-detectors-that","slug":"towards-universal-fake-image-detectors-that","title":"Towards Universal Fake Image Detectors that Generalize Across Generative Models","date":"2023-02-20","arxiv_id":"2302.10174","repositories_listed":2,"syntology":{"n":16,"n_ran":11,"n_constructed":0,"n_ran_checked":6,"n_instrument":5,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":14,"phrase":"11 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; 5 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/towards-universal-fake-image-detectors-that#ran","syntology_url":"https://syntology.ai/paper/2302.10174","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.10174"}},"official":{"repos":["yuheng-li/universalfakedetect"],"state":"official (archive's flag): 11 ran","n_ran":11,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/bbt-fin-comprehensive-construction-of-chinese","slug":"bbt-fin-comprehensive-construction-of-chinese","title":"BBT-Fin: Comprehensive Construction of Chinese Financial Domain Pre-trained Language Model, Corpus and Benchmark","date":"2023-02-18","arxiv_id":"2302.09432","repositories_listed":2,"syntology":null},{"url":"/paper/learning-performance-improving-code-edits","slug":"learning-performance-improving-code-edits","title":"Learning Performance-Improving Code Edits","date":"2023-02-15","arxiv_id":"2302.07867","repositories_listed":2,"syntology":{"n":19,"n_ran":8,"n_constructed":0,"n_ran_checked":3,"n_instrument":5,"n_unverified":11,"n_honours":2,"n_violates":0,"n_no_contract":1,"n_pointer_only":19,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 2 honoured, 0 violated, 1 with no contract checked; 5 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/learning-performance-improving-code-edits#ran","syntology_url":"https://syntology.ai/paper/2302.07867","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.07867"}},"official":{"repos":["madaan/pie-perf"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":11,"ran_from_kinds":["official"]}}},{"url":"/paper/adaptive-test-generation-using-a-large","slug":"adaptive-test-generation-using-a-large","title":"An Empirical Evaluation of Using Large Language Models for Automated Unit Test Generation","date":"2023-02-13","arxiv_id":"2302.06527","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/adaptive-test-generation-using-a-large#ran","syntology_url":"https://syntology.ai/paper/2302.06527","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.06527"}},"official":{"repos":["githubnext/testpilot"],"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/pre-train-prompt-and-recommendation-a","slug":"pre-train-prompt-and-recommendation-a","title":"Pre-train, Prompt and Recommendation: A Comprehensive Survey of Language Modelling Paradigm Adaptations in Recommender Systems","date":"2023-02-07","arxiv_id":"2302.03735","repositories_listed":2,"syntology":null},{"url":"/paper/grounding-language-models-to-images-for","slug":"grounding-language-models-to-images-for","title":"Grounding Language Models to Images for Multimodal Inputs and Outputs","date":"2023-01-31","arxiv_id":"2301.13823","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":5,"n_pointer_only":1,"phrase":"6 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; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/grounding-language-models-to-images-for#ran","syntology_url":"https://syntology.ai/paper/2301.13823","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.13823"}},"official":{"repos":["kohjingyu/fromage"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/in-context-retrieval-augmented-language","slug":"in-context-retrieval-augmented-language","title":"In-Context Retrieval-Augmented Language Models","date":"2023-01-31","arxiv_id":"2302.00083","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":0,"n_instrument":3,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":1,"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) · 1 unverified","sample_list":"/paper/in-context-retrieval-augmented-language#ran","syntology_url":"https://syntology.ai/paper/2302.00083","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2302.00083"}},"official":{"repos":["ai21labs/in-context-ralm"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/knowledge-transfer-from-pre-trained-language","slug":"knowledge-transfer-from-pre-trained-language","title":"Knowledge Transfer from Pre-trained Language Models to Cif-based Speech Recognizers via Hierarchical Distillation","date":"2023-01-30","arxiv_id":"2301.13003","repositories_listed":2,"syntology":null},{"url":"/paper/vicarious-offense-and-noise-audit-of","slug":"vicarious-offense-and-noise-audit-of","title":"Vicarious Offense and Noise Audit of Offensive Speech Classifiers: Unifying Human and Machine Disagreement on What is Offensive","date":"2023-01-29","arxiv_id":"2301.12534","repositories_listed":2,"syntology":null},{"url":"/paper/byte-pair-encoding-for-symbolic-music","slug":"byte-pair-encoding-for-symbolic-music","title":"Byte Pair Encoding for Symbolic Music","date":"2023-01-27","arxiv_id":"2301.11975","repositories_listed":2,"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":2,"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/byte-pair-encoding-for-symbolic-music#ran","syntology_url":"https://syntology.ai/paper/2301.11975","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2301.11975"}},"official":{"repos":["Natooz/MidiTok","natooz/bpe-symbolic-music"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/swarm-parallelism-training-large-models-can-1","slug":"swarm-parallelism-training-large-models-can-1","title":"SWARM Parallelism: Training Large Models Can Be Surprisingly Communication-Efficient","date":"2023-01-27","arxiv_id":"2301.11913","repositories_listed":2,"syntology":null},{"url":"/paper/editing-language-model-based-knowledge-graph","slug":"editing-language-model-based-knowledge-graph","title":"Editing Language Model-based Knowledge Graph Embeddings","date":"2023-01-25","arxiv_id":"2301.10405","repositories_listed":2,"syntology":null},{"url":"/paper/adapting-a-language-model-while-preserving","slug":"adapting-a-language-model-while-preserving","title":"Adapting a Language Model While Preserving its General Knowledge","date":"2023-01-21","arxiv_id":"2301.08986","repositories_listed":2,"syntology":null},{"url":"/paper/batch-prompting-efficient-inference-with","slug":"batch-prompting-efficient-inference-with","title":"Batch Prompting: Efficient Inference with Large Language Model APIs","date":"2023-01-19","arxiv_id":"2301.08721","repositories_listed":2,"syntology":null},{"url":"/paper/demonstrate-search-predict-composing","slug":"demonstrate-search-predict-composing","title":"Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP","date":"2022-12-28","arxiv_id":"2212.14024","repositories_listed":2,"syntology":null},{"url":"/paper/training-language-models-for-deeper","slug":"training-language-models-for-deeper","title":"Training language models to summarize narratives improves brain alignment","date":"2022-12-21","arxiv_id":"2212.10898","repositories_listed":2,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"n_honours":1,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"3 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; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/training-language-models-for-deeper#ran","syntology_url":"https://syntology.ai/paper/2212.10898","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.10898"}},"official":{"repos":["awwkl/brain_language_narratives","awwkl/brain_language_summarization"],"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/eit-enhanced-interactive-transformer","slug":"eit-enhanced-interactive-transformer","title":"EIT: Enhanced Interactive Transformer","date":"2022-12-20","arxiv_id":"2212.10197","repositories_listed":2,"syntology":null},{"url":"/paper/emergent-analogical-reasoning-in-large","slug":"emergent-analogical-reasoning-in-large","title":"Emergent Analogical Reasoning in Large Language Models","date":"2022-12-19","arxiv_id":"2212.09196","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/emergent-analogical-reasoning-in-large#ran","syntology_url":"https://syntology.ai/paper/2212.09196","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.09196"}},"official":{"repos":["taylorwwebb/emergent_analogies_llm"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}}],"record_sha256":"4130518201cf0393a80aad09cc2be2c63595aef5e807ba63362a3037bf6c9414","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}