{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3A2E4JE4ZXX5ZYST5GYDZV6UTE","short_pith_number":"pith:3A2E4JE4","schema_version":"1.0","canonical_sha256":"d8344e249ccdefdce253e9b03cd7d49908b73f9b47bad2a87a67d63d65e9891a","source":{"kind":"arxiv","id":"2411.09826","version":1},"attestation_state":"computed","paper":{"title":"Evaluating Gender Bias in Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Andreas M\\\"uller, Markus D\\\"ohring, Michael D\\\"oll","submitted_at":"2024-11-14T22:23:13Z","abstract_excerpt":"Gender bias in artificial intelligence has become an important issue, particularly in the context of language models used in communication-oriented applications. This study examines the extent to which Large Language Models (LLMs) exhibit gender bias in pronoun selection in occupational contexts. The analysis evaluates the models GPT-4, GPT-4o, PaLM 2 Text Bison and Gemini 1.0 Pro using a self-generated dataset. The jobs considered include a range of occupations, from those with a significant male presence to those with a notable female concentration, as well as jobs with a relatively equal ge"},"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":"2411.09826","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-14T22:23:13Z","cross_cats_sorted":[],"title_canon_sha256":"48c04b09065ab59ad6254aa0c341085275b8282bf9554aef98b72d622ce4b252","abstract_canon_sha256":"7c6d973705280f9f76f4e42b97fe0414be846e80c05fc32c98dae08cdd21bba5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:35:38.794300Z","signature_b64":"w0vZ986Z3Z4+X3Mtvvqqm1r0Qp/+83F6pOSCtH/wilccJMBXr4MQacaC/QtERYz6+rINZ6bhKzSzKJfmucEeAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d8344e249ccdefdce253e9b03cd7d49908b73f9b47bad2a87a67d63d65e9891a","last_reissued_at":"2026-07-05T09:35:38.793815Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:35:38.793815Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating Gender Bias in Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Andreas M\\\"uller, Markus D\\\"ohring, Michael D\\\"oll","submitted_at":"2024-11-14T22:23:13Z","abstract_excerpt":"Gender bias in artificial intelligence has become an important issue, particularly in the context of language models used in communication-oriented applications. This study examines the extent to which Large Language Models (LLMs) exhibit gender bias in pronoun selection in occupational contexts. The analysis evaluates the models GPT-4, GPT-4o, PaLM 2 Text Bison and Gemini 1.0 Pro using a self-generated dataset. The jobs considered include a range of occupations, from those with a significant male presence to those with a notable female concentration, as well as jobs with a relatively equal ge"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.09826","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/2411.09826/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":"2411.09826","created_at":"2026-07-05T09:35:38.793889+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.09826v1","created_at":"2026-07-05T09:35:38.793889+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.09826","created_at":"2026-07-05T09:35:38.793889+00:00"},{"alias_kind":"pith_short_12","alias_value":"3A2E4JE4ZXX5","created_at":"2026-07-05T09:35:38.793889+00:00"},{"alias_kind":"pith_short_16","alias_value":"3A2E4JE4ZXX5ZYST","created_at":"2026-07-05T09:35:38.793889+00:00"},{"alias_kind":"pith_short_8","alias_value":"3A2E4JE4","created_at":"2026-07-05T09:35:38.793889+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2508.09651","citing_title":"A Close Reading Approach to Gender Narrative Biases in AI-Generated Stories","ref_index":11,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3A2E4JE4ZXX5ZYST5GYDZV6UTE","json":"https://pith.science/pith/3A2E4JE4ZXX5ZYST5GYDZV6UTE.json","graph_json":"https://pith.science/api/pith-number/3A2E4JE4ZXX5ZYST5GYDZV6UTE/graph.json","events_json":"https://pith.science/api/pith-number/3A2E4JE4ZXX5ZYST5GYDZV6UTE/events.json","paper":"https://pith.science/paper/3A2E4JE4"},"agent_actions":{"view_html":"https://pith.science/pith/3A2E4JE4ZXX5ZYST5GYDZV6UTE","download_json":"https://pith.science/pith/3A2E4JE4ZXX5ZYST5GYDZV6UTE.json","view_paper":"https://pith.science/paper/3A2E4JE4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.09826&json=true","fetch_graph":"https://pith.science/api/pith-number/3A2E4JE4ZXX5ZYST5GYDZV6UTE/graph.json","fetch_events":"https://pith.science/api/pith-number/3A2E4JE4ZXX5ZYST5GYDZV6UTE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3A2E4JE4ZXX5ZYST5GYDZV6UTE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3A2E4JE4ZXX5ZYST5GYDZV6UTE/action/storage_attestation","attest_author":"https://pith.science/pith/3A2E4JE4ZXX5ZYST5GYDZV6UTE/action/author_attestation","sign_citation":"https://pith.science/pith/3A2E4JE4ZXX5ZYST5GYDZV6UTE/action/citation_signature","submit_replication":"https://pith.science/pith/3A2E4JE4ZXX5ZYST5GYDZV6UTE/action/replication_record"}},"created_at":"2026-07-05T09:35:38.793889+00:00","updated_at":"2026-07-05T09:35:38.793889+00:00"}