{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:J7RF7UWCQJTDGALGNQMPIMUP5M","short_pith_number":"pith:J7RF7UWC","schema_version":"1.0","canonical_sha256":"4fe25fd2c282663301666c18f4328feb353af2d0fd3d43484ec43e76c309adb0","source":{"kind":"arxiv","id":"1911.03064","version":3},"attestation_state":"computed","paper":{"title":"Reducing Sentiment Bias in Language Models via Counterfactual Evaluation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY","cs.LG"],"primary_cat":"cs.CL","authors_text":"Dani Yogatama, Huan Zhang, Jack Rae, Johannes Welbl, Po-Sen Huang, Pushmeet Kohli, Ray Jiang, Robert Stanforth, Vishal Maini","submitted_at":"2019-11-08T05:56:01Z","abstract_excerpt":"Advances in language modeling architectures and the availability of large text corpora have driven progress in automatic text generation. While this results in models capable of generating coherent texts, it also prompts models to internalize social biases present in the training corpus. This paper aims to quantify and reduce a particular type of bias exhibited by language models: bias in the sentiment of generated text. Given a conditioning context (e.g., a writing prompt) and a language model, we analyze if (and how) the sentiment of the generated text is affected by changes in values of sen"},"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":"1911.03064","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-11-08T05:56:01Z","cross_cats_sorted":["cs.CY","cs.LG"],"title_canon_sha256":"14544bb5ed21408ddfea1177ebc56c366afae7b0909db7091a50ef91aa319b5c","abstract_canon_sha256":"7efdba8139e9fc8f98e7f5885b7c2d8a0c135835ff50d82d858c482ba77bc118"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:41:15.813577Z","signature_b64":"SBqjycQcRN7ci47/hUVTkJikb98ndCjMed1zmFqT1vRukVNdzrnPvrKNytm03mVjBx42hG3JszpzNL68ANdaBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4fe25fd2c282663301666c18f4328feb353af2d0fd3d43484ec43e76c309adb0","last_reissued_at":"2026-07-05T01:41:15.813181Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:41:15.813181Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reducing Sentiment Bias in Language Models via Counterfactual Evaluation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY","cs.LG"],"primary_cat":"cs.CL","authors_text":"Dani Yogatama, Huan Zhang, Jack Rae, Johannes Welbl, Po-Sen Huang, Pushmeet Kohli, Ray Jiang, Robert Stanforth, Vishal Maini","submitted_at":"2019-11-08T05:56:01Z","abstract_excerpt":"Advances in language modeling architectures and the availability of large text corpora have driven progress in automatic text generation. While this results in models capable of generating coherent texts, it also prompts models to internalize social biases present in the training corpus. This paper aims to quantify and reduce a particular type of bias exhibited by language models: bias in the sentiment of generated text. Given a conditioning context (e.g., a writing prompt) and a language model, we analyze if (and how) the sentiment of the generated text is affected by changes in values of sen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1911.03064","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/1911.03064/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":"1911.03064","created_at":"2026-07-05T01:41:15.813239+00:00"},{"alias_kind":"arxiv_version","alias_value":"1911.03064v3","created_at":"2026-07-05T01:41:15.813239+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1911.03064","created_at":"2026-07-05T01:41:15.813239+00:00"},{"alias_kind":"pith_short_12","alias_value":"J7RF7UWCQJTD","created_at":"2026-07-05T01:41:15.813239+00:00"},{"alias_kind":"pith_short_16","alias_value":"J7RF7UWCQJTDGALG","created_at":"2026-07-05T01:41:15.813239+00:00"},{"alias_kind":"pith_short_8","alias_value":"J7RF7UWC","created_at":"2026-07-05T01:41:15.813239+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2508.06649","citing_title":"Measuring Stereotype and Deviation Biases in Large Language Models","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2112.04359","citing_title":"Ethical and social risks of harm from Language Models","ref_index":120,"is_internal_anchor":false},{"citing_arxiv_id":"2005.14165","citing_title":"Language Models are Few-Shot Learners","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J7RF7UWCQJTDGALGNQMPIMUP5M","json":"https://pith.science/pith/J7RF7UWCQJTDGALGNQMPIMUP5M.json","graph_json":"https://pith.science/api/pith-number/J7RF7UWCQJTDGALGNQMPIMUP5M/graph.json","events_json":"https://pith.science/api/pith-number/J7RF7UWCQJTDGALGNQMPIMUP5M/events.json","paper":"https://pith.science/paper/J7RF7UWC"},"agent_actions":{"view_html":"https://pith.science/pith/J7RF7UWCQJTDGALGNQMPIMUP5M","download_json":"https://pith.science/pith/J7RF7UWCQJTDGALGNQMPIMUP5M.json","view_paper":"https://pith.science/paper/J7RF7UWC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1911.03064&json=true","fetch_graph":"https://pith.science/api/pith-number/J7RF7UWCQJTDGALGNQMPIMUP5M/graph.json","fetch_events":"https://pith.science/api/pith-number/J7RF7UWCQJTDGALGNQMPIMUP5M/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J7RF7UWCQJTDGALGNQMPIMUP5M/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J7RF7UWCQJTDGALGNQMPIMUP5M/action/storage_attestation","attest_author":"https://pith.science/pith/J7RF7UWCQJTDGALGNQMPIMUP5M/action/author_attestation","sign_citation":"https://pith.science/pith/J7RF7UWCQJTDGALGNQMPIMUP5M/action/citation_signature","submit_replication":"https://pith.science/pith/J7RF7UWCQJTDGALGNQMPIMUP5M/action/replication_record"}},"created_at":"2026-07-05T01:41:15.813239+00:00","updated_at":"2026-07-05T01:41:15.813239+00:00"}