{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:S4R3WHYRMGMXUBCHFZC77I5VIR","short_pith_number":"pith:S4R3WHYR","schema_version":"1.0","canonical_sha256":"9723bb1f1161997a04472e45ffa3b5447b85f8a10ed32df551716e5a41fa8213","source":{"kind":"arxiv","id":"2404.00216","version":2},"attestation_state":"computed","paper":{"title":"Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Baolong Bi, Hongcheng Gao, Junfeng Fang, Lingrui Mei, Shenghua Liu, Shiyu Ni, Xueqi Cheng, Yiwei Wang","submitted_at":"2024-03-30T02:08:28Z","abstract_excerpt":"As the modern tools of choice for text understanding and generation, large language models (LLMs) are expected to accurately output answers by leveraging the input context. This requires LLMs to possess both context-faithfulness and factual accuracy. Extensive efforts have been made to enable better outputs from LLMs by mitigating hallucinations through factuality enhancement methods. However, they also pose risks of hindering context-faithfulness, as factuality enhancement can lead LLMs to become overly confident in their parametric knowledge, causing them to overlook the relevant input conte"},"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":"2404.00216","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2024-03-30T02:08:28Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0a7d5534d667386b7f4b6392bad6de62413e681100cda972f12a9a51c044ca53","abstract_canon_sha256":"5a373117fc15a0654754cef3272853cbf60a35b7cfdad2a767def548a59077cb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:15:30.961471Z","signature_b64":"XWILLWwksVJWjfsu7knJGsx9VmDIwfcmOGjTzcRQ4SoiumqJrN89BcruDmLHJYfAKSdE1xNUrx7z3f/90FgbDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9723bb1f1161997a04472e45ffa3b5447b85f8a10ed32df551716e5a41fa8213","last_reissued_at":"2026-07-05T09:15:30.960950Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:15:30.960950Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Is Factuality Enhancement a Free Lunch For LLMs? Better Factuality Can Lead to Worse Context-Faithfulness","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Baolong Bi, Hongcheng Gao, Junfeng Fang, Lingrui Mei, Shenghua Liu, Shiyu Ni, Xueqi Cheng, Yiwei Wang","submitted_at":"2024-03-30T02:08:28Z","abstract_excerpt":"As the modern tools of choice for text understanding and generation, large language models (LLMs) are expected to accurately output answers by leveraging the input context. This requires LLMs to possess both context-faithfulness and factual accuracy. Extensive efforts have been made to enable better outputs from LLMs by mitigating hallucinations through factuality enhancement methods. However, they also pose risks of hindering context-faithfulness, as factuality enhancement can lead LLMs to become overly confident in their parametric knowledge, causing them to overlook the relevant input conte"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2404.00216","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/2404.00216/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":"2404.00216","created_at":"2026-07-05T09:15:30.961010+00:00"},{"alias_kind":"arxiv_version","alias_value":"2404.00216v2","created_at":"2026-07-05T09:15:30.961010+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2404.00216","created_at":"2026-07-05T09:15:30.961010+00:00"},{"alias_kind":"pith_short_12","alias_value":"S4R3WHYRMGMX","created_at":"2026-07-05T09:15:30.961010+00:00"},{"alias_kind":"pith_short_16","alias_value":"S4R3WHYRMGMXUBCH","created_at":"2026-07-05T09:15:30.961010+00:00"},{"alias_kind":"pith_short_8","alias_value":"S4R3WHYR","created_at":"2026-07-05T09:15:30.961010+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/S4R3WHYRMGMXUBCHFZC77I5VIR","json":"https://pith.science/pith/S4R3WHYRMGMXUBCHFZC77I5VIR.json","graph_json":"https://pith.science/api/pith-number/S4R3WHYRMGMXUBCHFZC77I5VIR/graph.json","events_json":"https://pith.science/api/pith-number/S4R3WHYRMGMXUBCHFZC77I5VIR/events.json","paper":"https://pith.science/paper/S4R3WHYR"},"agent_actions":{"view_html":"https://pith.science/pith/S4R3WHYRMGMXUBCHFZC77I5VIR","download_json":"https://pith.science/pith/S4R3WHYRMGMXUBCHFZC77I5VIR.json","view_paper":"https://pith.science/paper/S4R3WHYR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2404.00216&json=true","fetch_graph":"https://pith.science/api/pith-number/S4R3WHYRMGMXUBCHFZC77I5VIR/graph.json","fetch_events":"https://pith.science/api/pith-number/S4R3WHYRMGMXUBCHFZC77I5VIR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S4R3WHYRMGMXUBCHFZC77I5VIR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S4R3WHYRMGMXUBCHFZC77I5VIR/action/storage_attestation","attest_author":"https://pith.science/pith/S4R3WHYRMGMXUBCHFZC77I5VIR/action/author_attestation","sign_citation":"https://pith.science/pith/S4R3WHYRMGMXUBCHFZC77I5VIR/action/citation_signature","submit_replication":"https://pith.science/pith/S4R3WHYRMGMXUBCHFZC77I5VIR/action/replication_record"}},"created_at":"2026-07-05T09:15:30.961010+00:00","updated_at":"2026-07-05T09:15:30.961010+00:00"}