{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:3S7G5E56MOWLNNLO3EAIV3ICKD","short_pith_number":"pith:3S7G5E56","schema_version":"1.0","canonical_sha256":"dcbe6e93be63acb6b56ed9008aed0250d91540750e9a9ad17587612c885ecb2d","source":{"kind":"arxiv","id":"2506.21443","version":1},"attestation_state":"computed","paper":{"title":"Domain Knowledge-Enhanced LLMs for Fraud and Concept Drift Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Ali \\c{S}enol, Garima Agrawal, Huan Liu","submitted_at":"2025-06-26T16:29:45Z","abstract_excerpt":"Detecting deceptive conversations on dynamic platforms is increasingly difficult due to evolving language patterns and Concept Drift (CD)-i.e., semantic or topical shifts that alter the context or intent of interactions over time. These shifts can obscure malicious intent or mimic normal dialogue, making accurate classification challenging. While Large Language Models (LLMs) show strong performance in natural language tasks, they often struggle with contextual ambiguity and hallucinations in risk-sensitive scenarios. To address these challenges, we present a Domain Knowledge (DK)-Enhanced LLM "},"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":"2506.21443","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2025-06-26T16:29:45Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"83be49938cff5cb365d9c4fa27ee4739f30e2d8417effe3605737e917e72884b","abstract_canon_sha256":"9ca16157bc9d56ec8ae8ae65d6bc4481011c2d0a968fa43ad88de3594551a4f1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-27T02:05:03.834954Z","signature_b64":"k/a1T0KcV0trBk6Y9uZcO2Dn67sbFqMnUSiLfmifXomgryBNiqCeXvWvajlSEkJ0hh+K9hqm45cL3pdHny9ZCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dcbe6e93be63acb6b56ed9008aed0250d91540750e9a9ad17587612c885ecb2d","last_reissued_at":"2026-05-27T02:05:03.834255Z","signature_status":"signed_v1","first_computed_at":"2026-05-27T02:05:03.834255Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Domain Knowledge-Enhanced LLMs for Fraud and Concept Drift Detection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Ali \\c{S}enol, Garima Agrawal, Huan Liu","submitted_at":"2025-06-26T16:29:45Z","abstract_excerpt":"Detecting deceptive conversations on dynamic platforms is increasingly difficult due to evolving language patterns and Concept Drift (CD)-i.e., semantic or topical shifts that alter the context or intent of interactions over time. These shifts can obscure malicious intent or mimic normal dialogue, making accurate classification challenging. While Large Language Models (LLMs) show strong performance in natural language tasks, they often struggle with contextual ambiguity and hallucinations in risk-sensitive scenarios. To address these challenges, we present a Domain Knowledge (DK)-Enhanced LLM "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.21443","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/2506.21443/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":"2506.21443","created_at":"2026-05-27T02:05:03.834364+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.21443v1","created_at":"2026-05-27T02:05:03.834364+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.21443","created_at":"2026-05-27T02:05:03.834364+00:00"},{"alias_kind":"pith_short_12","alias_value":"3S7G5E56MOWL","created_at":"2026-05-27T02:05:03.834364+00:00"},{"alias_kind":"pith_short_16","alias_value":"3S7G5E56MOWLNNLO","created_at":"2026-05-27T02:05:03.834364+00:00"},{"alias_kind":"pith_short_8","alias_value":"3S7G5E56","created_at":"2026-05-27T02:05:03.834364+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2602.12941","citing_title":"JARVIS: An Evidence-Grounded Retrieval System for Interpretable Deceptive Reviews Adjudication","ref_index":26,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3S7G5E56MOWLNNLO3EAIV3ICKD","json":"https://pith.science/pith/3S7G5E56MOWLNNLO3EAIV3ICKD.json","graph_json":"https://pith.science/api/pith-number/3S7G5E56MOWLNNLO3EAIV3ICKD/graph.json","events_json":"https://pith.science/api/pith-number/3S7G5E56MOWLNNLO3EAIV3ICKD/events.json","paper":"https://pith.science/paper/3S7G5E56"},"agent_actions":{"view_html":"https://pith.science/pith/3S7G5E56MOWLNNLO3EAIV3ICKD","download_json":"https://pith.science/pith/3S7G5E56MOWLNNLO3EAIV3ICKD.json","view_paper":"https://pith.science/paper/3S7G5E56","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.21443&json=true","fetch_graph":"https://pith.science/api/pith-number/3S7G5E56MOWLNNLO3EAIV3ICKD/graph.json","fetch_events":"https://pith.science/api/pith-number/3S7G5E56MOWLNNLO3EAIV3ICKD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3S7G5E56MOWLNNLO3EAIV3ICKD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3S7G5E56MOWLNNLO3EAIV3ICKD/action/storage_attestation","attest_author":"https://pith.science/pith/3S7G5E56MOWLNNLO3EAIV3ICKD/action/author_attestation","sign_citation":"https://pith.science/pith/3S7G5E56MOWLNNLO3EAIV3ICKD/action/citation_signature","submit_replication":"https://pith.science/pith/3S7G5E56MOWLNNLO3EAIV3ICKD/action/replication_record"}},"created_at":"2026-05-27T02:05:03.834364+00:00","updated_at":"2026-05-27T02:05:03.834364+00:00"}