{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:PO5UVDGT6BSV7R52QTNSU76BPG","short_pith_number":"pith:PO5UVDGT","schema_version":"1.0","canonical_sha256":"7bbb4a8cd3f0655fc7ba84db2a7fc17990135f6c941d782ce7733a1b89f3a07c","source":{"kind":"arxiv","id":"2311.06697","version":1},"attestation_state":"computed","paper":{"title":"Trusted Source Alignment in Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Edward Clifford, Riley Matthews, Simon Baumgartner, Vasilisa Bashlovkina, William W. Cohen, Yennie Jun, Zhaobin Kuang","submitted_at":"2023-11-12T00:25:25Z","abstract_excerpt":"Large language models (LLMs) are trained on web-scale corpora that inevitably include contradictory factual information from sources of varying reliability. In this paper, we propose measuring an LLM property called trusted source alignment (TSA): the model's propensity to align with content produced by trusted publishers in the face of uncertainty or controversy. We present FactCheckQA, a TSA evaluation dataset based on a corpus of fact checking articles. We describe a simple protocol for evaluating TSA and offer a detailed analysis of design considerations including response extraction, clai"},"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":"2311.06697","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-11-12T00:25:25Z","cross_cats_sorted":[],"title_canon_sha256":"d83349b59547c2242eb7090758760ea20165af4466eba673064991b55e79ba2f","abstract_canon_sha256":"48d1dfebe0c242a43afa6484aac3c6a30f69d4778abd7bd8f735dd0625e3276e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:12:02.256675Z","signature_b64":"F+L3VE5H2hvJI/1mm3S0UGshM7N/biB+xOM64UWBTwaOfVvFAbGTjEVJCJO2NvfXAGPL8fJwDHITqFj3LAsDDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7bbb4a8cd3f0655fc7ba84db2a7fc17990135f6c941d782ce7733a1b89f3a07c","last_reissued_at":"2026-07-05T07:12:02.256254Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:12:02.256254Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Trusted Source Alignment in Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Edward Clifford, Riley Matthews, Simon Baumgartner, Vasilisa Bashlovkina, William W. Cohen, Yennie Jun, Zhaobin Kuang","submitted_at":"2023-11-12T00:25:25Z","abstract_excerpt":"Large language models (LLMs) are trained on web-scale corpora that inevitably include contradictory factual information from sources of varying reliability. In this paper, we propose measuring an LLM property called trusted source alignment (TSA): the model's propensity to align with content produced by trusted publishers in the face of uncertainty or controversy. We present FactCheckQA, a TSA evaluation dataset based on a corpus of fact checking articles. We describe a simple protocol for evaluating TSA and offer a detailed analysis of design considerations including response extraction, clai"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.06697","kind":"arxiv","version":1},"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/2311.06697/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":"2311.06697","created_at":"2026-07-05T07:12:02.256311+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.06697v1","created_at":"2026-07-05T07:12:02.256311+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.06697","created_at":"2026-07-05T07:12:02.256311+00:00"},{"alias_kind":"pith_short_12","alias_value":"PO5UVDGT6BSV","created_at":"2026-07-05T07:12:02.256311+00:00"},{"alias_kind":"pith_short_16","alias_value":"PO5UVDGT6BSV7R52","created_at":"2026-07-05T07:12:02.256311+00:00"},{"alias_kind":"pith_short_8","alias_value":"PO5UVDGT","created_at":"2026-07-05T07:12:02.256311+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.17762","citing_title":"Resolving Conflicting Evidence in Automated Fact-Checking: A Study on Retrieval-Augmented LLMs","ref_index":4,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/PO5UVDGT6BSV7R52QTNSU76BPG","json":"https://pith.science/pith/PO5UVDGT6BSV7R52QTNSU76BPG.json","graph_json":"https://pith.science/api/pith-number/PO5UVDGT6BSV7R52QTNSU76BPG/graph.json","events_json":"https://pith.science/api/pith-number/PO5UVDGT6BSV7R52QTNSU76BPG/events.json","paper":"https://pith.science/paper/PO5UVDGT"},"agent_actions":{"view_html":"https://pith.science/pith/PO5UVDGT6BSV7R52QTNSU76BPG","download_json":"https://pith.science/pith/PO5UVDGT6BSV7R52QTNSU76BPG.json","view_paper":"https://pith.science/paper/PO5UVDGT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.06697&json=true","fetch_graph":"https://pith.science/api/pith-number/PO5UVDGT6BSV7R52QTNSU76BPG/graph.json","fetch_events":"https://pith.science/api/pith-number/PO5UVDGT6BSV7R52QTNSU76BPG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PO5UVDGT6BSV7R52QTNSU76BPG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PO5UVDGT6BSV7R52QTNSU76BPG/action/storage_attestation","attest_author":"https://pith.science/pith/PO5UVDGT6BSV7R52QTNSU76BPG/action/author_attestation","sign_citation":"https://pith.science/pith/PO5UVDGT6BSV7R52QTNSU76BPG/action/citation_signature","submit_replication":"https://pith.science/pith/PO5UVDGT6BSV7R52QTNSU76BPG/action/replication_record"}},"created_at":"2026-07-05T07:12:02.256311+00:00","updated_at":"2026-07-05T07:12:02.256311+00:00"}