{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:T4WEBENTM3IOR5CK4LJRIX43S5","short_pith_number":"pith:T4WEBENT","schema_version":"1.0","canonical_sha256":"9f2c4091b366d0e8f44ae2d3145f9b97438675f2e2e9916b8ea9f7c068a2ce19","source":{"kind":"arxiv","id":"2506.12483","version":1},"attestation_state":"computed","paper":{"title":"MALM: A Multi-Information Adapter for Large Language Models to Mitigate Hallucination","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Ao Jia, Dawei Song, Guohui Yao, Haiming Wu, Songkun Ji, Yazhou Zhang","submitted_at":"2025-06-14T12:47:32Z","abstract_excerpt":"Large language models (LLMs) are prone to three types of hallucination: Input-Conflicting, Context-Conflicting and Fact-Conflicting hallucinations. The purpose of this study is to mitigate the different types of hallucination by exploiting the interdependence between them. For this purpose, we propose a Multi-Information Adapter for Large Language Models (MALM). This framework employs a tailored multi-graph learning approach designed to elucidate the interconnections between original inputs, contextual information, and external factual knowledge, thereby alleviating the three categories of hal"},"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":"2506.12483","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-14T12:47:32Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"1b52b0302e62093a1e77eb6e050104af981cfb626547139a9b95294a8bba3d4e","abstract_canon_sha256":"1d2d5fc28176561dfae281fcead9fbcd6f8a90cb7531337c510e73399ff08070"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:21:54.041149Z","signature_b64":"SboBAgElygHXEEEzsDnrlbHnPtoupokd3f2ZDMsFUkAsV23gwiqGuSw7MbMNT3m4kEvcpJNHrU2DS4WRmoW1Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9f2c4091b366d0e8f44ae2d3145f9b97438675f2e2e9916b8ea9f7c068a2ce19","last_reissued_at":"2026-07-05T11:21:54.040652Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:21:54.040652Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MALM: A Multi-Information Adapter for Large Language Models to Mitigate Hallucination","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.AI","authors_text":"Ao Jia, Dawei Song, Guohui Yao, Haiming Wu, Songkun Ji, Yazhou Zhang","submitted_at":"2025-06-14T12:47:32Z","abstract_excerpt":"Large language models (LLMs) are prone to three types of hallucination: Input-Conflicting, Context-Conflicting and Fact-Conflicting hallucinations. The purpose of this study is to mitigate the different types of hallucination by exploiting the interdependence between them. For this purpose, we propose a Multi-Information Adapter for Large Language Models (MALM). This framework employs a tailored multi-graph learning approach designed to elucidate the interconnections between original inputs, contextual information, and external factual knowledge, thereby alleviating the three categories of hal"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12483","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/2506.12483/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":"2506.12483","created_at":"2026-07-05T11:21:54.040712+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.12483v1","created_at":"2026-07-05T11:21:54.040712+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12483","created_at":"2026-07-05T11:21:54.040712+00:00"},{"alias_kind":"pith_short_12","alias_value":"T4WEBENTM3IO","created_at":"2026-07-05T11:21:54.040712+00:00"},{"alias_kind":"pith_short_16","alias_value":"T4WEBENTM3IOR5CK","created_at":"2026-07-05T11:21:54.040712+00:00"},{"alias_kind":"pith_short_8","alias_value":"T4WEBENT","created_at":"2026-07-05T11:21:54.040712+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/T4WEBENTM3IOR5CK4LJRIX43S5","json":"https://pith.science/pith/T4WEBENTM3IOR5CK4LJRIX43S5.json","graph_json":"https://pith.science/api/pith-number/T4WEBENTM3IOR5CK4LJRIX43S5/graph.json","events_json":"https://pith.science/api/pith-number/T4WEBENTM3IOR5CK4LJRIX43S5/events.json","paper":"https://pith.science/paper/T4WEBENT"},"agent_actions":{"view_html":"https://pith.science/pith/T4WEBENTM3IOR5CK4LJRIX43S5","download_json":"https://pith.science/pith/T4WEBENTM3IOR5CK4LJRIX43S5.json","view_paper":"https://pith.science/paper/T4WEBENT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.12483&json=true","fetch_graph":"https://pith.science/api/pith-number/T4WEBENTM3IOR5CK4LJRIX43S5/graph.json","fetch_events":"https://pith.science/api/pith-number/T4WEBENTM3IOR5CK4LJRIX43S5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/T4WEBENTM3IOR5CK4LJRIX43S5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/T4WEBENTM3IOR5CK4LJRIX43S5/action/storage_attestation","attest_author":"https://pith.science/pith/T4WEBENTM3IOR5CK4LJRIX43S5/action/author_attestation","sign_citation":"https://pith.science/pith/T4WEBENTM3IOR5CK4LJRIX43S5/action/citation_signature","submit_replication":"https://pith.science/pith/T4WEBENTM3IOR5CK4LJRIX43S5/action/replication_record"}},"created_at":"2026-07-05T11:21:54.040712+00:00","updated_at":"2026-07-05T11:21:54.040712+00:00"}