{"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/bias-detection/papers/ran/1","list_of":"/task/bias-detection","task":"Bias Detection","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":[1,11],"of":11,"counts":{"archive_papers_tagged":199,"with_a_code_link":80,"where_syntology_ran_a_sample":11,"not_listed_spam_title":0,"listed":199,"listed_where_code_ran":11,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":10,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":10,"listed_every_run_a_failure_of_syntologys_instrument":1,"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/bias-detection/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/mitigating-bias-in-queer-representation","slug":"mitigating-bias-in-queer-representation","title":"Mitigating Bias in Queer Representation within Large Language Models: A Collaborative Agent Approach","date":"2024-11-12","arxiv_id":"2411.07656","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/mitigating-bias-in-queer-representation#ran","syntology_url":"https://syntology.ai/paper/2411.07656","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.07656"}},"official":{"repos":["tonyhrule/queer-bias-llms"],"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/towards-implicit-bias-detection-and","slug":"towards-implicit-bias-detection-and","title":"Towards Implicit Bias Detection and Mitigation in Multi-Agent LLM Interactions","date":"2024-10-03","arxiv_id":"2410.02584","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":1,"n_pointer_only":1,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 0 violated, 1 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/towards-implicit-bias-detection-and#ran","syntology_url":"https://syntology.ai/paper/2410.02584","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.02584"}},"official":{"repos":["MichiganNLP/MultiAgent_ImplicitBias"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/counterfactual-token-generation-in-large","slug":"counterfactual-token-generation-in-large","title":"Counterfactual Token Generation in Large Language Models","date":"2024-09-25","arxiv_id":"2409.17027","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":9,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":10,"phrase":"10 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/counterfactual-token-generation-in-large#ran","syntology_url":"https://syntology.ai/paper/2409.17027","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.17027"}},"official":{"repos":["networks-learning/counterfactual-llms"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/benchmarking-llama2-mistral-gemma-and-gpt-for","slug":"benchmarking-llama2-mistral-gemma-and-gpt-for","title":"Benchmarking Llama2, Mistral, Gemma and GPT for Factuality, Toxicity, Bias and Propensity for Hallucinations","date":"2024-04-15","arxiv_id":"2404.09785","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/benchmarking-llama2-mistral-gemma-and-gpt-for#ran","syntology_url":"https://syntology.ai/paper/2404.09785","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.09785"}},"official":{"repos":["innodatalabs/innodata-llm-safety"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/openbias-open-set-bias-detection-in-text-to","slug":"openbias-open-set-bias-detection-in-text-to","title":"OpenBias: Open-set Bias Detection in Text-to-Image Generative Models","date":"2024-04-11","arxiv_id":"2404.07990","repositories_listed":1,"syntology":{"n":10,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":7,"n_pointer_only":10,"phrase":"10 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/openbias-open-set-bias-detection-in-text-to#ran","syntology_url":"https://syntology.ai/paper/2404.07990","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.07990"}},"official":{"repos":["picsart-ai-research/openbias"],"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/denetdm-debiasing-by-network-depth-modulation","slug":"denetdm-debiasing-by-network-depth-modulation","title":"DeNetDM: Debiasing by Network Depth Modulation","date":"2024-03-28","arxiv_id":"2403.19863","repositories_listed":1,"syntology":{"n":7,"n_ran":6,"n_constructed":0,"n_ran_checked":5,"n_instrument":1,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/denetdm-debiasing-by-network-depth-modulation#ran","syntology_url":"https://syntology.ai/paper/2403.19863","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.19863"}},"official":{"repos":["kadarsh22/denetdm"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":5,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/trade-offs-between-fairness-and-privacy-in","slug":"trade-offs-between-fairness-and-privacy-in","title":"Trade-Offs Between Fairness and Privacy in Language Modeling","date":"2023-05-24","arxiv_id":"2305.14936","repositories_listed":1,"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/trade-offs-between-fairness-and-privacy-in#ran","syntology_url":"https://syntology.ai/paper/2305.14936","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.14936"}},"official":{"repos":["cleolotta/fair-and-private-lm"],"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","unlocated"]}}},{"url":"/paper/galactica-a-large-language-model-for-science-1","slug":"galactica-a-large-language-model-for-science-1","title":"Galactica: A Large Language Model for Science","date":"2022-11-16","arxiv_id":"2211.09085","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/galactica-a-large-language-model-for-science-1#ran","syntology_url":"https://syntology.ai/paper/2211.09085","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2211.09085"}},"official":{"repos":["paperswithcode/galai"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/amazon-sagemaker-clarify-machine-learning","slug":"amazon-sagemaker-clarify-machine-learning","title":"Amazon SageMaker Clarify: Machine Learning Bias Detection and Explainability in the Cloud","date":"2021-09-07","arxiv_id":"2109.03285","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":1,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/amazon-sagemaker-clarify-machine-learning#ran","syntology_url":"https://syntology.ai/paper/2109.03285","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.03285"}},"official":{"repos":["aws/amazon-sagemaker-clarify"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/exploring-visual-engagement-signals-for","slug":"exploring-visual-engagement-signals-for","title":"Exploring Visual Engagement Signals for Representation Learning","date":"2021-04-15","arxiv_id":"2104.07767","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/exploring-visual-engagement-signals-for#ran","syntology_url":"https://syntology.ai/paper/2104.07767","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.07767"}},"official":{"repos":["KMnP/vise"],"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/detecting-emergent-intersectional-biases","slug":"detecting-emergent-intersectional-biases","title":"Detecting Emergent Intersectional Biases: Contextualized Word Embeddings Contain a Distribution of Human-like Biases","date":"2020-06-06","arxiv_id":"2006.03955","repositories_listed":2,"syntology":{"n":10,"n_ran":8,"n_constructed":0,"n_ran_checked":7,"n_instrument":1,"n_unverified":2,"n_honours":5,"n_violates":0,"n_no_contract":2,"n_pointer_only":6,"phrase":"8 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 5 honoured, 0 violated, 2 with no contract checked; 1 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/detecting-emergent-intersectional-biases#ran","syntology_url":"https://syntology.ai/paper/2006.03955","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.03955"}},"official":{"repos":["weiguowilliam/CEAT"],"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":["listed","official"]}}}],"record_sha256":"76cf9f2bb7426d69ecf51267b258231f349aa97fa66abd86a6eb85d88c6631d9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}