{"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/sts/papers/ran/1","list_of":"/task/sts","task":"STS","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":1,"rows_per_page":100,"rows":[1,28],"of":28,"counts":{"archive_papers_tagged":334,"with_a_code_link":129,"where_syntology_ran_a_sample":28,"not_listed_spam_title":0,"listed":334,"listed_where_code_ran":28,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":25,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":25,"listed_every_run_a_failure_of_syntologys_instrument":3,"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/sts/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/fast-precise-thompson-sampling-for-bayesian","slug":"fast-precise-thompson-sampling-for-bayesian","title":"Fast, Precise Thompson Sampling for Bayesian Optimization","date":"2024-11-26","arxiv_id":"2411.17071","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":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) · 1 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/fast-precise-thompson-sampling-for-bayesian#ran","syntology_url":"https://syntology.ai/paper/2411.17071","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.17071"}},"official":null}},{"url":"/paper/geneol-harnessing-the-generative-power-of","slug":"geneol-harnessing-the-generative-power-of","title":"GenEOL: Harnessing the Generative Power of LLMs for Training-Free Sentence Embeddings","date":"2024-10-18","arxiv_id":"2410.14635","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":3,"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/geneol-harnessing-the-generative-power-of#ran","syntology_url":"https://syntology.ai/paper/2410.14635","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.14635"}},"official":{"repos":["raghavlite/GenEOL"],"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/improving-multi-lingual-alignment-through","slug":"improving-multi-lingual-alignment-through","title":"Improving Multi-lingual Alignment Through Soft Contrastive Learning","date":"2024-05-25","arxiv_id":"2405.16155","repositories_listed":1,"syntology":{"n":10,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":10,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/improving-multi-lingual-alignment-through#ran","syntology_url":"https://syntology.ai/paper/2405.16155","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.16155"}},"official":{"repos":["yai12xlinq-b/imascl"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/manipulating-large-language-models-to","slug":"manipulating-large-language-models-to","title":"Manipulating Large Language Models to Increase Product Visibility","date":"2024-04-11","arxiv_id":"2404.07981","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/manipulating-large-language-models-to#ran","syntology_url":"https://syntology.ai/paper/2404.07981","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.07981"}},"official":{"repos":["aounon/llm-rank-optimizer"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/rar-b-reasoning-as-retrieval-benchmark","slug":"rar-b-reasoning-as-retrieval-benchmark","title":"RAR-b: Reasoning as Retrieval Benchmark","date":"2024-04-09","arxiv_id":"2404.06347","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":6,"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/rar-b-reasoning-as-retrieval-benchmark#ran","syntology_url":"https://syntology.ai/paper/2404.06347","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.06347"}},"official":{"repos":["gowitheflow-1998/rar-b"],"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/meta-task-prompting-elicits-embedding-from","slug":"meta-task-prompting-elicits-embedding-from","title":"Meta-Task Prompting Elicits Embeddings from Large Language Models","date":"2024-02-28","arxiv_id":"2402.18458","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":1,"n_no_contract":1,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 1 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/meta-task-prompting-elicits-embedding-from#ran","syntology_url":"https://syntology.ai/paper/2402.18458","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.18458"}},"official":{"repos":["yibin-lei/metaeol"],"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/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/deelm-dependency-enhanced-large-language","slug":"deelm-dependency-enhanced-large-language","title":"BeLLM: Backward Dependency Enhanced Large Language Model for Sentence Embeddings","date":"2023-11-09","arxiv_id":"2311.05296","repositories_listed":2,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/deelm-dependency-enhanced-large-language#ran","syntology_url":"https://syntology.ai/paper/2311.05296","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2311.05296"}},"official":{"repos":["4ai/bellm"],"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/scaling-sentence-embeddings-with-large","slug":"scaling-sentence-embeddings-with-large","title":"Scaling Sentence Embeddings with Large Language Models","date":"2023-07-31","arxiv_id":"2307.16645","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":0,"n_no_contract":4,"n_pointer_only":4,"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) · 0 unverified","sample_list":"/paper/scaling-sentence-embeddings-with-large#ran","syntology_url":"https://syntology.ai/paper/2307.16645","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2307.16645"}},"official":{"repos":["kongds/scaling_sentemb"],"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/csts-conditional-semantic-textual-similarity","slug":"csts-conditional-semantic-textual-similarity","title":"C-STS: Conditional Semantic Textual Similarity","date":"2023-05-24","arxiv_id":"2305.15093","repositories_listed":1,"syntology":{"n":4,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":4,"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) · 2 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/csts-conditional-semantic-textual-similarity#ran","syntology_url":"https://syntology.ai/paper/2305.15093","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.15093"}},"official":{"repos":["princeton-nlp/c-sts"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/english-contrastive-learning-can-learn","slug":"english-contrastive-learning-can-learn","title":"English Contrastive Learning Can Learn Universal Cross-lingual Sentence Embeddings","date":"2022-11-11","arxiv_id":"2211.06127","repositories_listed":1,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":6,"n_instrument":2,"n_unverified":2,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":5,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 2 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/english-contrastive-learning-can-learn#ran","syntology_url":"https://syntology.ai/paper/2211.06127","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.06127"}},"official":{"repos":["yaushian/msimcse"],"state":"official (archive's flag): 8 ran","n_ran":8,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/generate-discriminate-and-contrast-a-semi","slug":"generate-discriminate-and-contrast-a-semi","title":"Generate, Discriminate and Contrast: A Semi-Supervised Sentence Representation Learning Framework","date":"2022-10-30","arxiv_id":"2210.16798","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":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/generate-discriminate-and-contrast-a-semi#ran","syntology_url":"https://syntology.ai/paper/2210.16798","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.16798"}},"official":{"repos":["matthewcym/gense"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["found_in_text"]}}},{"url":"/paper/mteb-massive-text-embedding-benchmark","slug":"mteb-massive-text-embedding-benchmark","title":"MTEB: Massive Text Embedding Benchmark","date":"2022-10-13","arxiv_id":"2210.07316","repositories_listed":5,"syntology":{"n":13,"n_ran":10,"n_constructed":0,"n_ran_checked":7,"n_instrument":3,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 3 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/mteb-massive-text-embedding-benchmark#ran","syntology_url":"https://syntology.ai/paper/2210.07316","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2210.07316"}},"official":{"repos":["embeddings-benchmark/mteb"],"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/ranksim-ranking-similarity-regularization-for","slug":"ranksim-ranking-similarity-regularization-for","title":"RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression","date":"2022-05-30","arxiv_id":"2205.15236","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":1,"n_ran_checked":1,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":4,"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) · 1 unverified","sample_list":"/paper/ranksim-ranking-similarity-regularization-for#ran","syntology_url":"https://syntology.ai/paper/2205.15236","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.15236"}},"official":{"repos":["BorealisAI/ranksim-imbalanced-regression"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/ease-entity-aware-contrastive-learning-of-1","slug":"ease-entity-aware-contrastive-learning-of-1","title":"EASE: Entity-Aware Contrastive Learning of Sentence Embedding","date":"2022-05-09","arxiv_id":"2205.04260","repositories_listed":1,"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":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) · 4 unverified","sample_list":"/paper/ease-entity-aware-contrastive-learning-of-1#ran","syntology_url":"https://syntology.ai/paper/2205.04260","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.04260"}},"official":{"repos":["studio-ousia/ease"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/a-sentence-is-worth-128-pseudo-tokens-a-1","slug":"a-sentence-is-worth-128-pseudo-tokens-a-1","title":"A Sentence is Worth 128 Pseudo Tokens: A Semantic-Aware Contrastive Learning Framework for Sentence Embeddings","date":"2022-03-11","arxiv_id":"2203.05877","repositories_listed":1,"syntology":{"n":3,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"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) · 2 unverified","sample_list":"/paper/a-sentence-is-worth-128-pseudo-tokens-a-1#ran","syntology_url":"https://syntology.ai/paper/2203.05877","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2203.05877"}},"official":{"repos":["namco0816/pt-bert"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-the-impact-of-negative-samples-of-1","slug":"exploring-the-impact-of-negative-samples-of-1","title":"Exploring the Impact of Negative Samples of Contrastive Learning: A Case Study of Sentence Embedding","date":"2022-02-26","arxiv_id":"2202.13093","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":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/exploring-the-impact-of-negative-samples-of-1#ran","syntology_url":"https://syntology.ai/paper/2202.13093","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.13093"}},"official":{"repos":["xbdxwyh/mocose"],"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/toward-interpretable-semantic-textual","slug":"toward-interpretable-semantic-textual","title":"Toward Interpretable Semantic Textual Similarity via Optimal Transport-based Contrastive Sentence Learning","date":"2022-02-26","arxiv_id":"2202.13196","repositories_listed":1,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":1,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":3,"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) · 0 unverified","sample_list":"/paper/toward-interpretable-semantic-textual#ran","syntology_url":"https://syntology.ai/paper/2202.13196","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2202.13196"}},"official":{"repos":["sh0416/clrcmd"],"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":["official"]}}},{"url":"/paper/promptbert-improving-bert-sentence-embeddings-1","slug":"promptbert-improving-bert-sentence-embeddings-1","title":"PromptBERT: Improving BERT Sentence Embeddings with Prompts","date":"2022-01-12","arxiv_id":"2201.04337","repositories_listed":1,"syntology":{"n":5,"n_ran":1,"n_constructed":1,"n_ran_checked":1,"n_instrument":0,"n_unverified":4,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":5,"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) · 4 unverified; the one sample that ran constructed an object rather than computing a result","sample_list":"/paper/promptbert-improving-bert-sentence-embeddings-1#ran","syntology_url":"https://syntology.ai/paper/2201.04337","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.04337"}},"official":{"repos":["kongds/prompt-bert"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":1,"n_ran_no_instrument_failure":1,"n_unverified":4,"ran_from_kinds":["official"]}}},{"url":"/paper/space-time-separable-graph-convolutional-1","slug":"space-time-separable-graph-convolutional-1","title":"Space-Time-Separable Graph Convolutional Network for Pose Forecasting","date":"2021-10-09","arxiv_id":"2110.04573","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"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 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) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/space-time-separable-graph-convolutional-1#ran","syntology_url":"https://syntology.ai/paper/2110.04573","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.04573"}},"official":{"repos":["fraluca/stsgcn"],"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":["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/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/simcse-simple-contrastive-learning-of","slug":"simcse-simple-contrastive-learning-of","title":"SimCSE: Simple Contrastive Learning of Sentence Embeddings","date":"2021-04-18","arxiv_id":"2104.08821","repositories_listed":23,"syntology":{"n":30,"n_ran":17,"n_constructed":9,"n_ran_checked":12,"n_instrument":5,"n_unverified":13,"n_honours":1,"n_violates":0,"n_no_contract":11,"n_pointer_only":19,"phrase":"17 ran (of which 9 constructed an object rather than computing a result; 12 with no instrument failure: 1 honoured, 0 violated, 11 with no contract checked; 5 where Syntology's instrument failed) · 13 unverified","sample_list":"/paper/simcse-simple-contrastive-learning-of#ran","syntology_url":"https://syntology.ai/paper/2104.08821","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.08821"}},"official":{"repos":["princeton-nlp/SimCSE"],"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/tsdae-using-transformer-based-sequential","slug":"tsdae-using-transformer-based-sequential","title":"TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning","date":"2021-04-14","arxiv_id":"2104.06979","repositories_listed":6,"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/tsdae-using-transformer-based-sequential#ran","syntology_url":"https://syntology.ai/paper/2104.06979","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.06979"}},"official":{"repos":["kwang2049/pytorch-bertflow","kwang2049/useb","ukplab/pytorch-bertflow"],"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/learning-intuitive-physics-with-multimodal","slug":"learning-intuitive-physics-with-multimodal","title":"Learning Intuitive Physics with Multimodal Generative Models","date":"2021-01-12","arxiv_id":"2101.04454","repositories_listed":1,"syntology":{"n":8,"n_ran":6,"n_constructed":1,"n_ran_checked":5,"n_instrument":1,"n_unverified":2,"n_honours":1,"n_violates":0,"n_no_contract":4,"n_pointer_only":8,"phrase":"6 ran (of which 1 constructed an object rather than computing a result; 5 with no instrument failure: 1 honoured, 0 violated, 4 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/learning-intuitive-physics-with-multimodal#ran","syntology_url":"https://syntology.ai/paper/2101.04454","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2101.04454"}},"official":{"repos":["SAIC-MONTREAL/multimodal-dynamics"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":1,"n_ran_no_instrument_failure":5,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/sentence-bert-sentence-embeddings-using","slug":"sentence-bert-sentence-embeddings-using","title":"Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks","date":"2019-08-27","arxiv_id":"1908.10084","repositories_listed":64,"syntology":{"n":58,"n_ran":33,"n_constructed":9,"n_ran_checked":30,"n_instrument":3,"n_unverified":25,"n_honours":1,"n_violates":0,"n_no_contract":29,"n_pointer_only":11,"phrase":"33 ran (of which 9 constructed an object rather than computing a result; 30 with no instrument failure: 1 honoured, 0 violated, 29 with no contract checked; 3 where Syntology's instrument failed) · 25 unverified","sample_list":"/paper/sentence-bert-sentence-embeddings-using#ran","syntology_url":"https://syntology.ai/paper/1908.10084","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1908.10084"}},"official":{"repos":["UKPLab/sentence-transformers"],"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/learning-semantic-textual-similarity-from","slug":"learning-semantic-textual-similarity-from","title":"Learning Semantic Textual Similarity from Conversations","date":"2018-04-20","arxiv_id":"1804.07754","repositories_listed":1,"syntology":{"n":17,"n_ran":15,"n_constructed":0,"n_ran_checked":15,"n_instrument":0,"n_unverified":2,"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) · 2 unverified","sample_list":"/paper/learning-semantic-textual-similarity-from#ran","syntology_url":"https://syntology.ai/paper/1804.07754","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1804.07754"}},"official":null}}],"record_sha256":"5fa8c638dec8c8877977ad1d032c123156ace6ff2bbb7a12d42d45dd5f89dd13","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}