{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7KO7FR5KN4AWVX76WFVLCIEFWG","short_pith_number":"pith:7KO7FR5K","schema_version":"1.0","canonical_sha256":"fa9df2c7aa6f016adffeb16ab12085b19b236b08649e02cab12b644473010f3d","source":{"kind":"arxiv","id":"2503.08588","version":1},"attestation_state":"computed","paper":{"title":"BiasEdit: Debiasing Stereotyped Language Models via Model Editing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.LG"],"primary_cat":"cs.CL","authors_text":"Julian McAuley, Ningyu Zhang, Wei Xu, Xin Xu","submitted_at":"2025-03-11T16:25:36Z","abstract_excerpt":"Previous studies have established that language models manifest stereotyped biases. Existing debiasing strategies, such as retraining a model with counterfactual data, representation projection, and prompting often fail to efficiently eliminate bias or directly alter the models' biased internal representations. To address these issues, we propose BiasEdit, an efficient model editing method to remove stereotypical bias from language models through lightweight networks that act as editors to generate parameter updates. BiasEdit employs a debiasing loss guiding editor networks to conduct local ed"},"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":"2503.08588","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-03-11T16:25:36Z","cross_cats_sorted":["cs.AI","cs.CY","cs.LG"],"title_canon_sha256":"019c87b270412972b3bb3af437901edd84fcf5d036b323afd744468b11788c48","abstract_canon_sha256":"3b91638d2f59cd15734180e0edfd20f38f45f95e49863919ec36ca440f2cdee3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:29:02.967929Z","signature_b64":"Y95zZYatByPJIADnkkopiYslX8EvEFq3ZvxSnawTnnOBNr3wjdqUCN9JjmuYEhE7PFpHy1hwWGFkOy6uM/rrDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fa9df2c7aa6f016adffeb16ab12085b19b236b08649e02cab12b644473010f3d","last_reissued_at":"2026-07-05T10:29:02.967423Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:29:02.967423Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BiasEdit: Debiasing Stereotyped Language Models via Model Editing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.LG"],"primary_cat":"cs.CL","authors_text":"Julian McAuley, Ningyu Zhang, Wei Xu, Xin Xu","submitted_at":"2025-03-11T16:25:36Z","abstract_excerpt":"Previous studies have established that language models manifest stereotyped biases. Existing debiasing strategies, such as retraining a model with counterfactual data, representation projection, and prompting often fail to efficiently eliminate bias or directly alter the models' biased internal representations. To address these issues, we propose BiasEdit, an efficient model editing method to remove stereotypical bias from language models through lightweight networks that act as editors to generate parameter updates. BiasEdit employs a debiasing loss guiding editor networks to conduct local ed"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.08588","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/2503.08588/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":"2503.08588","created_at":"2026-07-05T10:29:02.967484+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.08588v1","created_at":"2026-07-05T10:29:02.967484+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.08588","created_at":"2026-07-05T10:29:02.967484+00:00"},{"alias_kind":"pith_short_12","alias_value":"7KO7FR5KN4AW","created_at":"2026-07-05T10:29:02.967484+00:00"},{"alias_kind":"pith_short_16","alias_value":"7KO7FR5KN4AWVX76","created_at":"2026-07-05T10:29:02.967484+00:00"},{"alias_kind":"pith_short_8","alias_value":"7KO7FR5K","created_at":"2026-07-05T10:29:02.967484+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2507.03724","citing_title":"MemOS: A Memory OS for AI System","ref_index":78,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7KO7FR5KN4AWVX76WFVLCIEFWG","json":"https://pith.science/pith/7KO7FR5KN4AWVX76WFVLCIEFWG.json","graph_json":"https://pith.science/api/pith-number/7KO7FR5KN4AWVX76WFVLCIEFWG/graph.json","events_json":"https://pith.science/api/pith-number/7KO7FR5KN4AWVX76WFVLCIEFWG/events.json","paper":"https://pith.science/paper/7KO7FR5K"},"agent_actions":{"view_html":"https://pith.science/pith/7KO7FR5KN4AWVX76WFVLCIEFWG","download_json":"https://pith.science/pith/7KO7FR5KN4AWVX76WFVLCIEFWG.json","view_paper":"https://pith.science/paper/7KO7FR5K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.08588&json=true","fetch_graph":"https://pith.science/api/pith-number/7KO7FR5KN4AWVX76WFVLCIEFWG/graph.json","fetch_events":"https://pith.science/api/pith-number/7KO7FR5KN4AWVX76WFVLCIEFWG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7KO7FR5KN4AWVX76WFVLCIEFWG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7KO7FR5KN4AWVX76WFVLCIEFWG/action/storage_attestation","attest_author":"https://pith.science/pith/7KO7FR5KN4AWVX76WFVLCIEFWG/action/author_attestation","sign_citation":"https://pith.science/pith/7KO7FR5KN4AWVX76WFVLCIEFWG/action/citation_signature","submit_replication":"https://pith.science/pith/7KO7FR5KN4AWVX76WFVLCIEFWG/action/replication_record"}},"created_at":"2026-07-05T10:29:02.967484+00:00","updated_at":"2026-07-05T10:29:02.967484+00:00"}