{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:B5A5ASFDLS5U5MTLX56ZTDZIOG","short_pith_number":"pith:B5A5ASFD","schema_version":"1.0","canonical_sha256":"0f41d048a35cbb4eb26bbf7d998f2871ae33452b0b1dd227ee7a629b5afe57bf","source":{"kind":"arxiv","id":"2310.00935","version":3},"attestation_state":"computed","paper":{"title":"Resolving Knowledge Conflicts in Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Heng Wang, Shangbin Feng, Tianxing He, Vidhisha Balachandran, Weijia Shi, Yike Wang, Yulia Tsvetkov","submitted_at":"2023-10-02T06:57:45Z","abstract_excerpt":"Large language models (LLMs) often encounter knowledge conflicts, scenarios where discrepancy arises between the internal parametric knowledge of LLMs and non-parametric information provided in the prompt context. In this work we ask what are the desiderata for LLMs when a knowledge conflict arises and whether existing LLMs fulfill them. We posit that LLMs should 1) identify knowledge conflicts, 2) pinpoint conflicting information segments, and 3) provide distinct answers or viewpoints in conflicting scenarios. To this end, we introduce an evaluation framework for simulating contextual knowled"},"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":"2310.00935","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-10-02T06:57:45Z","cross_cats_sorted":[],"title_canon_sha256":"da3eae3a45ded987af2aa207eaacca57c7759d1780119cbe7f391c2585ed758c","abstract_canon_sha256":"38151abbe48e55dbcacc13f77dfddc3c0d159b903e8c999da6f22dea4a96ec8e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:20:37.724085Z","signature_b64":"dGzdKUoZoZcHOjgHmBgHrbyq+eyX0FnyEBZ2ZbKGZJGtA//eZkYw6Y/1Y9lMabOoikIbeac9zYPamiy4+mSlAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0f41d048a35cbb4eb26bbf7d998f2871ae33452b0b1dd227ee7a629b5afe57bf","last_reissued_at":"2026-07-05T09:20:37.723647Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:20:37.723647Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Resolving Knowledge Conflicts in Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Heng Wang, Shangbin Feng, Tianxing He, Vidhisha Balachandran, Weijia Shi, Yike Wang, Yulia Tsvetkov","submitted_at":"2023-10-02T06:57:45Z","abstract_excerpt":"Large language models (LLMs) often encounter knowledge conflicts, scenarios where discrepancy arises between the internal parametric knowledge of LLMs and non-parametric information provided in the prompt context. In this work we ask what are the desiderata for LLMs when a knowledge conflict arises and whether existing LLMs fulfill them. We posit that LLMs should 1) identify knowledge conflicts, 2) pinpoint conflicting information segments, and 3) provide distinct answers or viewpoints in conflicting scenarios. To this end, we introduce an evaluation framework for simulating contextual knowled"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.00935","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/2310.00935/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":"2310.00935","created_at":"2026-07-05T09:20:37.723706+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.00935v3","created_at":"2026-07-05T09:20:37.723706+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.00935","created_at":"2026-07-05T09:20:37.723706+00:00"},{"alias_kind":"pith_short_12","alias_value":"B5A5ASFDLS5U","created_at":"2026-07-05T09:20:37.723706+00:00"},{"alias_kind":"pith_short_16","alias_value":"B5A5ASFDLS5U5MTL","created_at":"2026-07-05T09:20:37.723706+00:00"},{"alias_kind":"pith_short_8","alias_value":"B5A5ASFD","created_at":"2026-07-05T09:20:37.723706+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":9,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.17301","citing_title":"ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2606.27786","citing_title":"SHIFT: Gate-Modulated Activation Steering for Knowledge Conflict Mitigation in Retrieval-Augmented Generation","ref_index":73,"is_internal_anchor":false},{"citing_arxiv_id":"2606.20245","citing_title":"Navigating Unreliable Parametric and Contextual Knowledge: Explicit Knowledge Conflict Resolution for LLM Inference","ref_index":3,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17301","citing_title":"ConflictRAG: Detecting and Resolving Knowledge Conflicts in Retrieval Augmented Generation","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20855","citing_title":"Caesar: Deep Agentic Web Exploration for Creative Answer Synthesis","ref_index":5,"is_internal_anchor":false},{"citing_arxiv_id":"2604.14172","citing_title":"Tug-of-War within A Decade: Conflict Resolution in Vulnerability Analysis via Teacher-Guided Retrieval-Augmented Generations","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12185","citing_title":"Mitigating Context-Memory Conflicts in LLMs through Dynamic Cognitive Reconciliation Decoding","ref_index":8,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09278","citing_title":"EquiMem: Calibrating Shared Memory in Multi-Agent Debate via Game-Theoretic Equilibrium","ref_index":74,"is_internal_anchor":false},{"citing_arxiv_id":"2604.22193","citing_title":"How Large Language Models Balance Internal Knowledge with User and Document Assertions","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/B5A5ASFDLS5U5MTLX56ZTDZIOG","json":"https://pith.science/pith/B5A5ASFDLS5U5MTLX56ZTDZIOG.json","graph_json":"https://pith.science/api/pith-number/B5A5ASFDLS5U5MTLX56ZTDZIOG/graph.json","events_json":"https://pith.science/api/pith-number/B5A5ASFDLS5U5MTLX56ZTDZIOG/events.json","paper":"https://pith.science/paper/B5A5ASFD"},"agent_actions":{"view_html":"https://pith.science/pith/B5A5ASFDLS5U5MTLX56ZTDZIOG","download_json":"https://pith.science/pith/B5A5ASFDLS5U5MTLX56ZTDZIOG.json","view_paper":"https://pith.science/paper/B5A5ASFD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.00935&json=true","fetch_graph":"https://pith.science/api/pith-number/B5A5ASFDLS5U5MTLX56ZTDZIOG/graph.json","fetch_events":"https://pith.science/api/pith-number/B5A5ASFDLS5U5MTLX56ZTDZIOG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B5A5ASFDLS5U5MTLX56ZTDZIOG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B5A5ASFDLS5U5MTLX56ZTDZIOG/action/storage_attestation","attest_author":"https://pith.science/pith/B5A5ASFDLS5U5MTLX56ZTDZIOG/action/author_attestation","sign_citation":"https://pith.science/pith/B5A5ASFDLS5U5MTLX56ZTDZIOG/action/citation_signature","submit_replication":"https://pith.science/pith/B5A5ASFDLS5U5MTLX56ZTDZIOG/action/replication_record"}},"created_at":"2026-07-05T09:20:37.723706+00:00","updated_at":"2026-07-05T09:20:37.723706+00:00"}