{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:J7O3ZYMD4INCJMJ3PKKBOTJP5I","short_pith_number":"pith:J7O3ZYMD","schema_version":"1.0","canonical_sha256":"4fddbce183e21a24b13b7a94174d2fea263895f82c2f9bb92d8bfa5f2735cca7","source":{"kind":"arxiv","id":"2408.15533","version":3},"attestation_state":"computed","paper":{"title":"LRP4RAG: Detecting Hallucinations in Retrieval-Augmented Generation via Layer-wise Relevance Propagation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Congqing He, Haichuan Hu, Quanjun Zhang, Xiaochen Xie","submitted_at":"2024-08-28T04:44:43Z","abstract_excerpt":"Retrieval-Augmented Generation (RAG) has become a primary technique for mitigating hallucinations in large language models (LLMs). However, incomplete knowledge extraction and insufficient understanding can still mislead LLMs to produce irrelevant or even contradictory responses, which means hallucinations persist in RAG. In this paper, we propose LRP4RAG, a method based on the Layer-wise Relevance Propagation (LRP) algorithm for detecting hallucinations in RAG. Specifically, we first utilize LRP to compute the relevance between the input and output of the RAG generator. We then apply further "},"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":"2408.15533","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-08-28T04:44:43Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"933afcdcb7f1f7cf6b14bd256c0d63d35d7af80d40dce2306720f86b81503bab","abstract_canon_sha256":"021cffe041e0555c8a7874c1ecca891bbc90096c12a058531e7c6f15c1467e69"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:28:11.134675Z","signature_b64":"JK0UXEHm1OvGbdgrZryLYUsHETLX2yYhYps8CbhZRGa+CvLgDPImdzDUdFQN8Cyq2LJPYLzGu5/g4llmnCbdDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4fddbce183e21a24b13b7a94174d2fea263895f82c2f9bb92d8bfa5f2735cca7","last_reissued_at":"2026-07-05T11:28:11.134172Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:28:11.134172Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LRP4RAG: Detecting Hallucinations in Retrieval-Augmented Generation via Layer-wise Relevance Propagation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Congqing He, Haichuan Hu, Quanjun Zhang, Xiaochen Xie","submitted_at":"2024-08-28T04:44:43Z","abstract_excerpt":"Retrieval-Augmented Generation (RAG) has become a primary technique for mitigating hallucinations in large language models (LLMs). However, incomplete knowledge extraction and insufficient understanding can still mislead LLMs to produce irrelevant or even contradictory responses, which means hallucinations persist in RAG. In this paper, we propose LRP4RAG, a method based on the Layer-wise Relevance Propagation (LRP) algorithm for detecting hallucinations in RAG. Specifically, we first utilize LRP to compute the relevance between the input and output of the RAG generator. We then apply further "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.15533","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/2408.15533/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":"2408.15533","created_at":"2026-07-05T11:28:11.134237+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.15533v3","created_at":"2026-07-05T11:28:11.134237+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.15533","created_at":"2026-07-05T11:28:11.134237+00:00"},{"alias_kind":"pith_short_12","alias_value":"J7O3ZYMD4INC","created_at":"2026-07-05T11:28:11.134237+00:00"},{"alias_kind":"pith_short_16","alias_value":"J7O3ZYMD4INCJMJ3","created_at":"2026-07-05T11:28:11.134237+00:00"},{"alias_kind":"pith_short_8","alias_value":"J7O3ZYMD","created_at":"2026-07-05T11:28:11.134237+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.06748","citing_title":"Evidence Graph Consistency in Retrieval-Augmented Generation: A Model-Dependent Analysis of Hallucination Detection","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2606.01581","citing_title":"Agent System Operations: Categorization, Challenges, and Future Directions","ref_index":91,"is_internal_anchor":false},{"citing_arxiv_id":"2606.06748","citing_title":"Evidence Graph Consistency in Retrieval-Augmented Generation: A Model-Dependent Analysis of Hallucination Detection","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2604.15945","citing_title":"RAGognizer: Hallucination-Aware Fine-Tuning via Detection Head Integration","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/J7O3ZYMD4INCJMJ3PKKBOTJP5I","json":"https://pith.science/pith/J7O3ZYMD4INCJMJ3PKKBOTJP5I.json","graph_json":"https://pith.science/api/pith-number/J7O3ZYMD4INCJMJ3PKKBOTJP5I/graph.json","events_json":"https://pith.science/api/pith-number/J7O3ZYMD4INCJMJ3PKKBOTJP5I/events.json","paper":"https://pith.science/paper/J7O3ZYMD"},"agent_actions":{"view_html":"https://pith.science/pith/J7O3ZYMD4INCJMJ3PKKBOTJP5I","download_json":"https://pith.science/pith/J7O3ZYMD4INCJMJ3PKKBOTJP5I.json","view_paper":"https://pith.science/paper/J7O3ZYMD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.15533&json=true","fetch_graph":"https://pith.science/api/pith-number/J7O3ZYMD4INCJMJ3PKKBOTJP5I/graph.json","fetch_events":"https://pith.science/api/pith-number/J7O3ZYMD4INCJMJ3PKKBOTJP5I/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/J7O3ZYMD4INCJMJ3PKKBOTJP5I/action/timestamp_anchor","attest_storage":"https://pith.science/pith/J7O3ZYMD4INCJMJ3PKKBOTJP5I/action/storage_attestation","attest_author":"https://pith.science/pith/J7O3ZYMD4INCJMJ3PKKBOTJP5I/action/author_attestation","sign_citation":"https://pith.science/pith/J7O3ZYMD4INCJMJ3PKKBOTJP5I/action/citation_signature","submit_replication":"https://pith.science/pith/J7O3ZYMD4INCJMJ3PKKBOTJP5I/action/replication_record"}},"created_at":"2026-07-05T11:28:11.134237+00:00","updated_at":"2026-07-05T11:28:11.134237+00:00"}