{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:EV7C47AE7Z4WTCLVJHOYKH7JWM","short_pith_number":"pith:EV7C47AE","schema_version":"1.0","canonical_sha256":"257e2e7c04fe7969897549dd851fe9b33778c0479daca884599097282cf4c782","source":{"kind":"arxiv","id":"2405.07623","version":8},"attestation_state":"computed","paper":{"title":"Optimizing Class-Level Probability Reweighting Coefficients for Equitable Prompting Accuracy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ruixi Lin, Yang You","submitted_at":"2024-05-13T10:30:33Z","abstract_excerpt":"Even as we engineer LLMs for alignment and safety, they often uncover biases from pre-training data's statistical regularities (from disproportionate co-occurrences to stereotypical associations mirroring human cognitive biases). This leads to persistent, uneven class accuracy in classification and QA. Such per-class accuracy disparities are not inherently resolved by architectural/training evolutions or data scaling, making post-hoc correction essential for equitable performance. To mitigate LLM class accuracy imbalance, we develop a post-hoc probability reweighting method that directly optim"},"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":"2405.07623","kind":"arxiv","version":8},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-05-13T10:30:33Z","cross_cats_sorted":[],"title_canon_sha256":"96450c45a5be5454654860b16dc3badfa211ec39909f4e69c1ba6c5bbc880758","abstract_canon_sha256":"7df425a5942aa3ccd99fba3f13d8cbdca394ec903f15672facac07b876cacfe7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:52:13.050883Z","signature_b64":"oHW0IkE4PAHnhJ+eseeAqxOvlRdtZacsCZgQmVNH2QVsQQvVSEL4irvfmM+g8djhfu8D7KTxqlbhmF8nLVTnDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"257e2e7c04fe7969897549dd851fe9b33778c0479daca884599097282cf4c782","last_reissued_at":"2026-07-05T11:52:13.050476Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:52:13.050476Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Optimizing Class-Level Probability Reweighting Coefficients for Equitable Prompting Accuracy","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Ruixi Lin, Yang You","submitted_at":"2024-05-13T10:30:33Z","abstract_excerpt":"Even as we engineer LLMs for alignment and safety, they often uncover biases from pre-training data's statistical regularities (from disproportionate co-occurrences to stereotypical associations mirroring human cognitive biases). This leads to persistent, uneven class accuracy in classification and QA. Such per-class accuracy disparities are not inherently resolved by architectural/training evolutions or data scaling, making post-hoc correction essential for equitable performance. To mitigate LLM class accuracy imbalance, we develop a post-hoc probability reweighting method that directly optim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.07623","kind":"arxiv","version":8},"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/2405.07623/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":"2405.07623","created_at":"2026-07-05T11:52:13.050531+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.07623v8","created_at":"2026-07-05T11:52:13.050531+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.07623","created_at":"2026-07-05T11:52:13.050531+00:00"},{"alias_kind":"pith_short_12","alias_value":"EV7C47AE7Z4W","created_at":"2026-07-05T11:52:13.050531+00:00"},{"alias_kind":"pith_short_16","alias_value":"EV7C47AE7Z4WTCLV","created_at":"2026-07-05T11:52:13.050531+00:00"},{"alias_kind":"pith_short_8","alias_value":"EV7C47AE","created_at":"2026-07-05T11:52:13.050531+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.02339","citing_title":"Entropy Minimization without Model Collapse: Mitigating Prediction Bias in Medical Imaging","ref_index":47,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EV7C47AE7Z4WTCLVJHOYKH7JWM","json":"https://pith.science/pith/EV7C47AE7Z4WTCLVJHOYKH7JWM.json","graph_json":"https://pith.science/api/pith-number/EV7C47AE7Z4WTCLVJHOYKH7JWM/graph.json","events_json":"https://pith.science/api/pith-number/EV7C47AE7Z4WTCLVJHOYKH7JWM/events.json","paper":"https://pith.science/paper/EV7C47AE"},"agent_actions":{"view_html":"https://pith.science/pith/EV7C47AE7Z4WTCLVJHOYKH7JWM","download_json":"https://pith.science/pith/EV7C47AE7Z4WTCLVJHOYKH7JWM.json","view_paper":"https://pith.science/paper/EV7C47AE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.07623&json=true","fetch_graph":"https://pith.science/api/pith-number/EV7C47AE7Z4WTCLVJHOYKH7JWM/graph.json","fetch_events":"https://pith.science/api/pith-number/EV7C47AE7Z4WTCLVJHOYKH7JWM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EV7C47AE7Z4WTCLVJHOYKH7JWM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EV7C47AE7Z4WTCLVJHOYKH7JWM/action/storage_attestation","attest_author":"https://pith.science/pith/EV7C47AE7Z4WTCLVJHOYKH7JWM/action/author_attestation","sign_citation":"https://pith.science/pith/EV7C47AE7Z4WTCLVJHOYKH7JWM/action/citation_signature","submit_replication":"https://pith.science/pith/EV7C47AE7Z4WTCLVJHOYKH7JWM/action/replication_record"}},"created_at":"2026-07-05T11:52:13.050531+00:00","updated_at":"2026-07-05T11:52:13.050531+00:00"}