{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:EGYDMXEZBJ5HF4D35M2L7C64OK","short_pith_number":"pith:EGYDMXEZ","schema_version":"1.0","canonical_sha256":"21b0365c990a7a72f07beb34bf8bdc7297da8d415fe4e74ae31f271fbdc5be8b","source":{"kind":"arxiv","id":"2309.07251","version":2},"attestation_state":"computed","paper":{"title":"In-Contextual Gender Bias Suppression for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Daisuke Oba, Danushka Bollegala, Masahiro Kaneko","submitted_at":"2023-09-13T18:39:08Z","abstract_excerpt":"Despite their impressive performance in a wide range of NLP tasks, Large Language Models (LLMs) have been reported to encode worrying-levels of gender biases. Prior work has proposed debiasing methods that require human labelled examples, data augmentation and fine-tuning of LLMs, which are computationally costly. Moreover, one might not even have access to the model parameters for performing debiasing such as in the case of closed LLMs such as GPT-4. To address this challenge, we propose bias suppression that prevents biased generations of LLMs by simply providing textual preambles constructe"},"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":"2309.07251","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2023-09-13T18:39:08Z","cross_cats_sorted":[],"title_canon_sha256":"76daa25f7ee637246808cb0ff50894b2ee08aa3ee716f41c7cfc0cc8e8917f10","abstract_canon_sha256":"2caa60bb3e0454c4e6de95b3acc310774dac95dbb2073b08255f7577adc6c0c7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:47:04.766557Z","signature_b64":"TgPLnSCDxXRgAae85diXwhyNasaqUlGKx4THolnhSJDLYRH5fQ1+Ie0ybtBax3pycxFdiE6FtvdgjiAzob4dDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21b0365c990a7a72f07beb34bf8bdc7297da8d415fe4e74ae31f271fbdc5be8b","last_reissued_at":"2026-07-05T07:47:04.766056Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:47:04.766056Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"In-Contextual Gender Bias Suppression for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Daisuke Oba, Danushka Bollegala, Masahiro Kaneko","submitted_at":"2023-09-13T18:39:08Z","abstract_excerpt":"Despite their impressive performance in a wide range of NLP tasks, Large Language Models (LLMs) have been reported to encode worrying-levels of gender biases. Prior work has proposed debiasing methods that require human labelled examples, data augmentation and fine-tuning of LLMs, which are computationally costly. Moreover, one might not even have access to the model parameters for performing debiasing such as in the case of closed LLMs such as GPT-4. To address this challenge, we propose bias suppression that prevents biased generations of LLMs by simply providing textual preambles constructe"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.07251","kind":"arxiv","version":2},"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/2309.07251/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":"2309.07251","created_at":"2026-07-05T07:47:04.766121+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.07251v2","created_at":"2026-07-05T07:47:04.766121+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.07251","created_at":"2026-07-05T07:47:04.766121+00:00"},{"alias_kind":"pith_short_12","alias_value":"EGYDMXEZBJ5H","created_at":"2026-07-05T07:47:04.766121+00:00"},{"alias_kind":"pith_short_16","alias_value":"EGYDMXEZBJ5HF4D3","created_at":"2026-07-05T07:47:04.766121+00:00"},{"alias_kind":"pith_short_8","alias_value":"EGYDMXEZ","created_at":"2026-07-05T07:47:04.766121+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.04515","citing_title":"Mitigation of Gender and Ethnicity Bias in AI-Generated Stories through Model Explanations","ref_index":2022,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EGYDMXEZBJ5HF4D35M2L7C64OK","json":"https://pith.science/pith/EGYDMXEZBJ5HF4D35M2L7C64OK.json","graph_json":"https://pith.science/api/pith-number/EGYDMXEZBJ5HF4D35M2L7C64OK/graph.json","events_json":"https://pith.science/api/pith-number/EGYDMXEZBJ5HF4D35M2L7C64OK/events.json","paper":"https://pith.science/paper/EGYDMXEZ"},"agent_actions":{"view_html":"https://pith.science/pith/EGYDMXEZBJ5HF4D35M2L7C64OK","download_json":"https://pith.science/pith/EGYDMXEZBJ5HF4D35M2L7C64OK.json","view_paper":"https://pith.science/paper/EGYDMXEZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.07251&json=true","fetch_graph":"https://pith.science/api/pith-number/EGYDMXEZBJ5HF4D35M2L7C64OK/graph.json","fetch_events":"https://pith.science/api/pith-number/EGYDMXEZBJ5HF4D35M2L7C64OK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EGYDMXEZBJ5HF4D35M2L7C64OK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EGYDMXEZBJ5HF4D35M2L7C64OK/action/storage_attestation","attest_author":"https://pith.science/pith/EGYDMXEZBJ5HF4D35M2L7C64OK/action/author_attestation","sign_citation":"https://pith.science/pith/EGYDMXEZBJ5HF4D35M2L7C64OK/action/citation_signature","submit_replication":"https://pith.science/pith/EGYDMXEZBJ5HF4D35M2L7C64OK/action/replication_record"}},"created_at":"2026-07-05T07:47:04.766121+00:00","updated_at":"2026-07-05T07:47:04.766121+00:00"}