{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:7BMRGDWQVQTEOSHEH6ZDLHLDFB","short_pith_number":"pith:7BMRGDWQ","schema_version":"1.0","canonical_sha256":"f859130ed0ac264748e43fb2359d63287cfaeb67d54cef071c8a2cb4beb084cb","source":{"kind":"arxiv","id":"2410.08820","version":3},"attestation_state":"computed","paper":{"title":"Which Demographics do LLMs Default to During Annotation?","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Aidan Combs, Amelie W\\\"uhrl, Aswathy Velutharambath, Christopher Bagdon, Jiahui Li, Johannes Sch\\\"afer, Lynn Greschner, Nadine Probol, Roman Klinger, Sabine Weber, Sean Papay, Yarik Menchaca Resendiz","submitted_at":"2024-10-11T14:02:42Z","abstract_excerpt":"Demographics and cultural background of annotators influence the labels they assign in text annotation -- for instance, an elderly woman might find it offensive to read a message addressed to a \"bro\", but a male teenager might find it appropriate. It is therefore important to acknowledge label variations to not under-represent members of a society. Two research directions developed out of this observation in the context of using large language models (LLM) for data annotations, namely (1) studying biases and inherent knowledge of LLMs and (2) injecting diversity in the output by manipulating t"},"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":"2410.08820","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-11T14:02:42Z","cross_cats_sorted":[],"title_canon_sha256":"81b4d097ece97732f224e1c5c6b2249cb02ad9c20e0f88f459487c04e58597d2","abstract_canon_sha256":"09f34be916482eb21a2b640c1e4cb781c02db16eb9831e2406a12632ab26f287"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:10:47.851724Z","signature_b64":"Ts9tVU6TQtM95yH6TIma2cOkLdIuZWVVb9EuXlbIVNr4LMD3D8JGx3cb9wiVRsUr5whK1AUcBcY4mriSBRBiAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f859130ed0ac264748e43fb2359d63287cfaeb67d54cef071c8a2cb4beb084cb","last_reissued_at":"2026-07-05T11:10:47.851157Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:10:47.851157Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Which Demographics do LLMs Default to During Annotation?","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Aidan Combs, Amelie W\\\"uhrl, Aswathy Velutharambath, Christopher Bagdon, Jiahui Li, Johannes Sch\\\"afer, Lynn Greschner, Nadine Probol, Roman Klinger, Sabine Weber, Sean Papay, Yarik Menchaca Resendiz","submitted_at":"2024-10-11T14:02:42Z","abstract_excerpt":"Demographics and cultural background of annotators influence the labels they assign in text annotation -- for instance, an elderly woman might find it offensive to read a message addressed to a \"bro\", but a male teenager might find it appropriate. It is therefore important to acknowledge label variations to not under-represent members of a society. Two research directions developed out of this observation in the context of using large language models (LLM) for data annotations, namely (1) studying biases and inherent knowledge of LLMs and (2) injecting diversity in the output by manipulating t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.08820","kind":"arxiv","version":3},"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/2410.08820/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":"2410.08820","created_at":"2026-07-05T11:10:47.851224+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.08820v3","created_at":"2026-07-05T11:10:47.851224+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.08820","created_at":"2026-07-05T11:10:47.851224+00:00"},{"alias_kind":"pith_short_12","alias_value":"7BMRGDWQVQTE","created_at":"2026-07-05T11:10:47.851224+00:00"},{"alias_kind":"pith_short_16","alias_value":"7BMRGDWQVQTEOSHE","created_at":"2026-07-05T11:10:47.851224+00:00"},{"alias_kind":"pith_short_8","alias_value":"7BMRGDWQ","created_at":"2026-07-05T11:10:47.851224+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.01168","citing_title":"Quantifying and Predicting Disagreement in Graded Human Ratings","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18069","citing_title":"Modeling Human Perspectives with Socio-Demographic Representations","ref_index":37,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7BMRGDWQVQTEOSHEH6ZDLHLDFB","json":"https://pith.science/pith/7BMRGDWQVQTEOSHEH6ZDLHLDFB.json","graph_json":"https://pith.science/api/pith-number/7BMRGDWQVQTEOSHEH6ZDLHLDFB/graph.json","events_json":"https://pith.science/api/pith-number/7BMRGDWQVQTEOSHEH6ZDLHLDFB/events.json","paper":"https://pith.science/paper/7BMRGDWQ"},"agent_actions":{"view_html":"https://pith.science/pith/7BMRGDWQVQTEOSHEH6ZDLHLDFB","download_json":"https://pith.science/pith/7BMRGDWQVQTEOSHEH6ZDLHLDFB.json","view_paper":"https://pith.science/paper/7BMRGDWQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.08820&json=true","fetch_graph":"https://pith.science/api/pith-number/7BMRGDWQVQTEOSHEH6ZDLHLDFB/graph.json","fetch_events":"https://pith.science/api/pith-number/7BMRGDWQVQTEOSHEH6ZDLHLDFB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7BMRGDWQVQTEOSHEH6ZDLHLDFB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7BMRGDWQVQTEOSHEH6ZDLHLDFB/action/storage_attestation","attest_author":"https://pith.science/pith/7BMRGDWQVQTEOSHEH6ZDLHLDFB/action/author_attestation","sign_citation":"https://pith.science/pith/7BMRGDWQVQTEOSHEH6ZDLHLDFB/action/citation_signature","submit_replication":"https://pith.science/pith/7BMRGDWQVQTEOSHEH6ZDLHLDFB/action/replication_record"}},"created_at":"2026-07-05T11:10:47.851224+00:00","updated_at":"2026-07-05T11:10:47.851224+00:00"}