{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:QVEWFR33GRSS3LMGLHEWLWEZ62","short_pith_number":"pith:QVEWFR33","schema_version":"1.0","canonical_sha256":"854962c77b34652dad8659c965d899f6838a9de2844512e62ebc61d0518f399e","source":{"kind":"arxiv","id":"2605.29523","version":1},"attestation_state":"computed","paper":{"title":"K-FinHallu: A Hallucination Detection Benchmark for Multi-Turn RAG in Korean Finance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Edward Choi, Eunbyeol Cho, Jeewon Yang, Mirae Kim, Youngjun Kwak, Yunseung Lee","submitted_at":"2026-05-28T07:40:19Z","abstract_excerpt":"Large Language Models (LLMs) have advanced financial automation through Retrieval-Augmented Generation (RAG), yet hallucinations remain a critical barrier to deployment in high-stakes environments. Existing benchmarks focus on single-turn, English-centric tasks, leaving the multi-turn dynamics and linguistic-regulatory nuances of the Korean financial domain unaddressed. We introduce K-FinHallu, the first benchmark for hallucination detection in multi-turn Korean financial RAG. We construct multi-turn dialogues from authentic Korean financial documents and inject hallucinations under a proposed"},"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":"2605.29523","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-05-28T07:40:19Z","cross_cats_sorted":[],"title_canon_sha256":"3cd36a8115147de305fd7bfec90f4a12582494d31af123c4ffda8a6492f7e467","abstract_canon_sha256":"de64cc676a94659638bca4aca0731fbc37329334e5aba2f506e2dd8b768e0440"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T01:05:44.892929Z","signature_b64":"ZtjUF0Pl6WWoTg9HKUDTYFZh/WJQ8JZarzX98g5rQHY6N7VebNPRkGGog5wwfpzdgcYql0xTWlIYAoWgDk4qCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"854962c77b34652dad8659c965d899f6838a9de2844512e62ebc61d0518f399e","last_reissued_at":"2026-05-29T01:05:44.892020Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T01:05:44.892020Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"K-FinHallu: A Hallucination Detection Benchmark for Multi-Turn RAG in Korean Finance","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Edward Choi, Eunbyeol Cho, Jeewon Yang, Mirae Kim, Youngjun Kwak, Yunseung Lee","submitted_at":"2026-05-28T07:40:19Z","abstract_excerpt":"Large Language Models (LLMs) have advanced financial automation through Retrieval-Augmented Generation (RAG), yet hallucinations remain a critical barrier to deployment in high-stakes environments. Existing benchmarks focus on single-turn, English-centric tasks, leaving the multi-turn dynamics and linguistic-regulatory nuances of the Korean financial domain unaddressed. We introduce K-FinHallu, the first benchmark for hallucination detection in multi-turn Korean financial RAG. We construct multi-turn dialogues from authentic Korean financial documents and inject hallucinations under a proposed"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.29523","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/2605.29523/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":"2605.29523","created_at":"2026-05-29T01:05:44.892168+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.29523v1","created_at":"2026-05-29T01:05:44.892168+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.29523","created_at":"2026-05-29T01:05:44.892168+00:00"},{"alias_kind":"pith_short_12","alias_value":"QVEWFR33GRSS","created_at":"2026-05-29T01:05:44.892168+00:00"},{"alias_kind":"pith_short_16","alias_value":"QVEWFR33GRSS3LMG","created_at":"2026-05-29T01:05:44.892168+00:00"},{"alias_kind":"pith_short_8","alias_value":"QVEWFR33","created_at":"2026-05-29T01:05:44.892168+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/QVEWFR33GRSS3LMGLHEWLWEZ62","json":"https://pith.science/pith/QVEWFR33GRSS3LMGLHEWLWEZ62.json","graph_json":"https://pith.science/api/pith-number/QVEWFR33GRSS3LMGLHEWLWEZ62/graph.json","events_json":"https://pith.science/api/pith-number/QVEWFR33GRSS3LMGLHEWLWEZ62/events.json","paper":"https://pith.science/paper/QVEWFR33"},"agent_actions":{"view_html":"https://pith.science/pith/QVEWFR33GRSS3LMGLHEWLWEZ62","download_json":"https://pith.science/pith/QVEWFR33GRSS3LMGLHEWLWEZ62.json","view_paper":"https://pith.science/paper/QVEWFR33","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.29523&json=true","fetch_graph":"https://pith.science/api/pith-number/QVEWFR33GRSS3LMGLHEWLWEZ62/graph.json","fetch_events":"https://pith.science/api/pith-number/QVEWFR33GRSS3LMGLHEWLWEZ62/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QVEWFR33GRSS3LMGLHEWLWEZ62/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QVEWFR33GRSS3LMGLHEWLWEZ62/action/storage_attestation","attest_author":"https://pith.science/pith/QVEWFR33GRSS3LMGLHEWLWEZ62/action/author_attestation","sign_citation":"https://pith.science/pith/QVEWFR33GRSS3LMGLHEWLWEZ62/action/citation_signature","submit_replication":"https://pith.science/pith/QVEWFR33GRSS3LMGLHEWLWEZ62/action/replication_record"}},"created_at":"2026-05-29T01:05:44.892168+00:00","updated_at":"2026-05-29T01:05:44.892168+00:00"}