{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7P3CSJ4PFO3ZPWAITCFXMWXJBP","short_pith_number":"pith:7P3CSJ4P","schema_version":"1.0","canonical_sha256":"fbf629278f2bb797d808988b765ae90bf777f7eb03c6d1229f9af35d72cb53fd","source":{"kind":"arxiv","id":"2403.18803","version":1},"attestation_state":"computed","paper":{"title":"Projective Methods for Mitigating Gender Bias in Pre-trained Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Daniel Gillis, Hillary Dawkins, Isar Nejadgholi, Judi McCuaig","submitted_at":"2024-03-27T17:49:31Z","abstract_excerpt":"Mitigation of gender bias in NLP has a long history tied to debiasing static word embeddings. More recently, attention has shifted to debiasing pre-trained language models. We study to what extent the simplest projective debiasing methods, developed for word embeddings, can help when applied to BERT's internal representations. Projective methods are fast to implement, use a small number of saved parameters, and make no updates to the existing model parameters. We evaluate the efficacy of the methods in reducing both intrinsic bias, as measured by BERT's next sentence prediction task, and in mi"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2403.18803","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-03-27T17:49:31Z","cross_cats_sorted":[],"title_canon_sha256":"323e06e8e08159c6372f6921889a5ac791e96d4ca792964c8b6efe83f6264169","abstract_canon_sha256":"afed85ad14c7223855112eefca1a97bbed450087e5b015f151a1283b05978d4d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:22:38.910556Z","signature_b64":"Vq0qnnYy2gM+qpGzF1su8w1cyyeFzByY1j8ZB5P5m9V0NHSD+ZZE+yY4MaaHKSLgFhVjnekc4xlJeYC20FH9Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fbf629278f2bb797d808988b765ae90bf777f7eb03c6d1229f9af35d72cb53fd","last_reissued_at":"2026-07-05T08:22:38.910045Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:22:38.910045Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Projective Methods for Mitigating Gender Bias in Pre-trained Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Daniel Gillis, Hillary Dawkins, Isar Nejadgholi, Judi McCuaig","submitted_at":"2024-03-27T17:49:31Z","abstract_excerpt":"Mitigation of gender bias in NLP has a long history tied to debiasing static word embeddings. More recently, attention has shifted to debiasing pre-trained language models. We study to what extent the simplest projective debiasing methods, developed for word embeddings, can help when applied to BERT's internal representations. Projective methods are fast to implement, use a small number of saved parameters, and make no updates to the existing model parameters. We evaluate the efficacy of the methods in reducing both intrinsic bias, as measured by BERT's next sentence prediction task, and in mi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.18803","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2403.18803/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2403.18803","created_at":"2026-07-05T08:22:38.910109+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.18803v1","created_at":"2026-07-05T08:22:38.910109+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.18803","created_at":"2026-07-05T08:22:38.910109+00:00"},{"alias_kind":"pith_short_12","alias_value":"7P3CSJ4PFO3Z","created_at":"2026-07-05T08:22:38.910109+00:00"},{"alias_kind":"pith_short_16","alias_value":"7P3CSJ4PFO3ZPWAI","created_at":"2026-07-05T08:22:38.910109+00:00"},{"alias_kind":"pith_short_8","alias_value":"7P3CSJ4P","created_at":"2026-07-05T08:22:38.910109+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.11111","citing_title":"Evaluating and Improving Robustness in Large Language Models: A Survey and Future Directions","ref_index":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7P3CSJ4PFO3ZPWAITCFXMWXJBP","json":"https://pith.science/pith/7P3CSJ4PFO3ZPWAITCFXMWXJBP.json","graph_json":"https://pith.science/api/pith-number/7P3CSJ4PFO3ZPWAITCFXMWXJBP/graph.json","events_json":"https://pith.science/api/pith-number/7P3CSJ4PFO3ZPWAITCFXMWXJBP/events.json","paper":"https://pith.science/paper/7P3CSJ4P"},"agent_actions":{"view_html":"https://pith.science/pith/7P3CSJ4PFO3ZPWAITCFXMWXJBP","download_json":"https://pith.science/pith/7P3CSJ4PFO3ZPWAITCFXMWXJBP.json","view_paper":"https://pith.science/paper/7P3CSJ4P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.18803&json=true","fetch_graph":"https://pith.science/api/pith-number/7P3CSJ4PFO3ZPWAITCFXMWXJBP/graph.json","fetch_events":"https://pith.science/api/pith-number/7P3CSJ4PFO3ZPWAITCFXMWXJBP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7P3CSJ4PFO3ZPWAITCFXMWXJBP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7P3CSJ4PFO3ZPWAITCFXMWXJBP/action/storage_attestation","attest_author":"https://pith.science/pith/7P3CSJ4PFO3ZPWAITCFXMWXJBP/action/author_attestation","sign_citation":"https://pith.science/pith/7P3CSJ4PFO3ZPWAITCFXMWXJBP/action/citation_signature","submit_replication":"https://pith.science/pith/7P3CSJ4PFO3ZPWAITCFXMWXJBP/action/replication_record"}},"created_at":"2026-07-05T08:22:38.910109+00:00","updated_at":"2026-07-05T08:22:38.910109+00:00"}