{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:67MWLUI2UB65LMIHFU6VJ7HFMH","short_pith_number":"pith:67MWLUI2","schema_version":"1.0","canonical_sha256":"f7d965d11aa07dd5b1072d3d54fce561c6c1f59e0d806558f6ae4cc049598b09","source":{"kind":"arxiv","id":"2411.04847","version":3},"attestation_state":"computed","paper":{"title":"Prompt-Guided Internal States for Hallucination Detection of Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Baolei Zhang, Biao Yi, Fujie Zhang, Peiqi Yu, Tong Li, Zheli Liu","submitted_at":"2024-11-07T16:33:48Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated remarkable capabilities across a variety of tasks in different domains. However, they sometimes generate responses that are logically coherent but factually incorrect or misleading, which is known as LLM hallucinations. Data-driven supervised methods train hallucination detectors by leveraging the internal states of LLMs, but detectors trained on specific domains often struggle to generalize well to other domains. In this paper, we aim to enhance the cross-domain performance of supervised detectors with only in-domain data. We propose a novel fram"},"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":"2411.04847","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-11-07T16:33:48Z","cross_cats_sorted":[],"title_canon_sha256":"b3b63956173a9e90d70ddda5ccc04a2cdb52eaca256118f616bb6a847f88426f","abstract_canon_sha256":"b8d8114246172d435ed3082d42937ae71d6f7e0c393821823215023f464acc5f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:07:16.443658Z","signature_b64":"g8vGXsfo6KO66BOgXC87FJdo+t3S+2GiFzTWSWQfs/BhloZs+BWjNX39bVnKG3MLIGvK18CBZQfMQBg2bvlxBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f7d965d11aa07dd5b1072d3d54fce561c6c1f59e0d806558f6ae4cc049598b09","last_reissued_at":"2026-07-05T11:07:16.443154Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:07:16.443154Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Prompt-Guided Internal States for Hallucination Detection of Large Language Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Baolei Zhang, Biao Yi, Fujie Zhang, Peiqi Yu, Tong Li, Zheli Liu","submitted_at":"2024-11-07T16:33:48Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated remarkable capabilities across a variety of tasks in different domains. However, they sometimes generate responses that are logically coherent but factually incorrect or misleading, which is known as LLM hallucinations. Data-driven supervised methods train hallucination detectors by leveraging the internal states of LLMs, but detectors trained on specific domains often struggle to generalize well to other domains. In this paper, we aim to enhance the cross-domain performance of supervised detectors with only in-domain data. We propose a novel fram"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.04847","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/2411.04847/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":"2411.04847","created_at":"2026-07-05T11:07:16.443212+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.04847v3","created_at":"2026-07-05T11:07:16.443212+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.04847","created_at":"2026-07-05T11:07:16.443212+00:00"},{"alias_kind":"pith_short_12","alias_value":"67MWLUI2UB65","created_at":"2026-07-05T11:07:16.443212+00:00"},{"alias_kind":"pith_short_16","alias_value":"67MWLUI2UB65LMIH","created_at":"2026-07-05T11:07:16.443212+00:00"},{"alias_kind":"pith_short_8","alias_value":"67MWLUI2","created_at":"2026-07-05T11:07:16.443212+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.03998","citing_title":"Toward Better Generalisation in Uncertainty Estimators: Leveraging Data-Agnostic Features","ref_index":35,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/67MWLUI2UB65LMIHFU6VJ7HFMH","json":"https://pith.science/pith/67MWLUI2UB65LMIHFU6VJ7HFMH.json","graph_json":"https://pith.science/api/pith-number/67MWLUI2UB65LMIHFU6VJ7HFMH/graph.json","events_json":"https://pith.science/api/pith-number/67MWLUI2UB65LMIHFU6VJ7HFMH/events.json","paper":"https://pith.science/paper/67MWLUI2"},"agent_actions":{"view_html":"https://pith.science/pith/67MWLUI2UB65LMIHFU6VJ7HFMH","download_json":"https://pith.science/pith/67MWLUI2UB65LMIHFU6VJ7HFMH.json","view_paper":"https://pith.science/paper/67MWLUI2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.04847&json=true","fetch_graph":"https://pith.science/api/pith-number/67MWLUI2UB65LMIHFU6VJ7HFMH/graph.json","fetch_events":"https://pith.science/api/pith-number/67MWLUI2UB65LMIHFU6VJ7HFMH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/67MWLUI2UB65LMIHFU6VJ7HFMH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/67MWLUI2UB65LMIHFU6VJ7HFMH/action/storage_attestation","attest_author":"https://pith.science/pith/67MWLUI2UB65LMIHFU6VJ7HFMH/action/author_attestation","sign_citation":"https://pith.science/pith/67MWLUI2UB65LMIHFU6VJ7HFMH/action/citation_signature","submit_replication":"https://pith.science/pith/67MWLUI2UB65LMIHFU6VJ7HFMH/action/replication_record"}},"created_at":"2026-07-05T11:07:16.443212+00:00","updated_at":"2026-07-05T11:07:16.443212+00:00"}