{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:J6BH6UAYYB2NKGED3J4CH4OIS7","short_pith_number":"pith:J6BH6UAY","canonical_record":{"source":{"id":"2410.23526","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-31T00:18:05Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a01fa8a99817eb38684468df83728cbecbbfdd60fc7d61b39249dc4d808fc141","abstract_canon_sha256":"d47ebbb7fa67a00b1c3693b68a6562c610935ca6353c2f567a5300d1e4f49cc0"},"schema_version":"1.0"},"canonical_sha256":"4f827f5018c074d51883da7823f1c897e0ec355b5e5b4b3f283d16c8304407a7","source":{"kind":"arxiv","id":"2410.23526","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.23526","created_at":"2026-07-05T09:29:01Z"},{"alias_kind":"arxiv_version","alias_value":"2410.23526v1","created_at":"2026-07-05T09:29:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.23526","created_at":"2026-07-05T09:29:01Z"},{"alias_kind":"pith_short_12","alias_value":"J6BH6UAYYB2N","created_at":"2026-07-05T09:29:01Z"},{"alias_kind":"pith_short_16","alias_value":"J6BH6UAYYB2NKGED","created_at":"2026-07-05T09:29:01Z"},{"alias_kind":"pith_short_8","alias_value":"J6BH6UAY","created_at":"2026-07-05T09:29:01Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:J6BH6UAYYB2NKGED3J4CH4OIS7","target":"record","payload":{"canonical_record":{"source":{"id":"2410.23526","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-31T00:18:05Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"a01fa8a99817eb38684468df83728cbecbbfdd60fc7d61b39249dc4d808fc141","abstract_canon_sha256":"d47ebbb7fa67a00b1c3693b68a6562c610935ca6353c2f567a5300d1e4f49cc0"},"schema_version":"1.0"},"canonical_sha256":"4f827f5018c074d51883da7823f1c897e0ec355b5e5b4b3f283d16c8304407a7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:29:01.579641Z","signature_b64":"rNspmmLXLlTPoj/dSiZG51YFLHATf1+hkf7rMietztvxmmzZYeKdMsDyoAoDLa16Jv+WAFknv7YDZ7U5bZDIDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4f827f5018c074d51883da7823f1c897e0ec355b5e5b4b3f283d16c8304407a7","last_reissued_at":"2026-07-05T09:29:01.579105Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:29:01.579105Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2410.23526","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:29:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7tb5rSLG12HtojLts3wcxFA/mbky8M3BUyqBIWrekdtguOSA2VgOl7q8VtKyB/4Cgx2H0BD91D5L4dLf9ROuDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T00:50:51.021476Z"},"content_sha256":"33b2c77012a852f24f7a40f5bf804e2905bd1633c78e4eb688cd46101ecaa100","schema_version":"1.0","event_id":"sha256:33b2c77012a852f24f7a40f5bf804e2905bd1633c78e4eb688cd46101ecaa100"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:J6BH6UAYYB2NKGED3J4CH4OIS7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Hieu Tran, Junda Wang, Terrence Chen, Weijing Huang, Yujan Ting","submitted_at":"2024-10-31T00:18:05Z","abstract_excerpt":"Large language models (LLMs) have shown remarkable capabilities in various natural language processing tasks, yet they often struggle with maintaining factual accuracy, particularly in knowledge-intensive domains like healthcare. This study introduces LEAF: Learning and Evaluation Augmented by Fact-Checking, a novel approach designed to enhance the factual reliability of LLMs, with a focus on medical question answering (QA). LEAF utilizes a dual strategy to enhance the factual accuracy of responses from models such as Llama 3 70B Instruct and Llama 3 8B Instruct. The first strategy, Fact-Check"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.23526","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/2410.23526/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T09:29:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fQIhZf1lOk6AyReqcexWszHjPvI6SqReCkc1BeMdMRLpJffKozgt2Fez5HChk6ZSZPCY0CNi2Kf+K5PRU8pYDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T00:50:51.022332Z"},"content_sha256":"82440d64ef91bf5240defc20944b7236478f6d8f072b423a874ac2e139077868","schema_version":"1.0","event_id":"sha256:82440d64ef91bf5240defc20944b7236478f6d8f072b423a874ac2e139077868"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/J6BH6UAYYB2NKGED3J4CH4OIS7/bundle.json","state_url":"https://pith.science/pith/J6BH6UAYYB2NKGED3J4CH4OIS7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/J6BH6UAYYB2NKGED3J4CH4OIS7/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-11T00:50:51Z","links":{"resolver":"https://pith.science/pith/J6BH6UAYYB2NKGED3J4CH4OIS7","bundle":"https://pith.science/pith/J6BH6UAYYB2NKGED3J4CH4OIS7/bundle.json","state":"https://pith.science/pith/J6BH6UAYYB2NKGED3J4CH4OIS7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/J6BH6UAYYB2NKGED3J4CH4OIS7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:J6BH6UAYYB2NKGED3J4CH4OIS7","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"d47ebbb7fa67a00b1c3693b68a6562c610935ca6353c2f567a5300d1e4f49cc0","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-31T00:18:05Z","title_canon_sha256":"a01fa8a99817eb38684468df83728cbecbbfdd60fc7d61b39249dc4d808fc141"},"schema_version":"1.0","source":{"id":"2410.23526","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2410.23526","created_at":"2026-07-05T09:29:01Z"},{"alias_kind":"arxiv_version","alias_value":"2410.23526v1","created_at":"2026-07-05T09:29:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.23526","created_at":"2026-07-05T09:29:01Z"},{"alias_kind":"pith_short_12","alias_value":"J6BH6UAYYB2N","created_at":"2026-07-05T09:29:01Z"},{"alias_kind":"pith_short_16","alias_value":"J6BH6UAYYB2NKGED","created_at":"2026-07-05T09:29:01Z"},{"alias_kind":"pith_short_8","alias_value":"J6BH6UAY","created_at":"2026-07-05T09:29:01Z"}],"graph_snapshots":[{"event_id":"sha256:82440d64ef91bf5240defc20944b7236478f6d8f072b423a874ac2e139077868","target":"graph","created_at":"2026-07-05T09:29:01Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2410.23526/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models (LLMs) have shown remarkable capabilities in various natural language processing tasks, yet they often struggle with maintaining factual accuracy, particularly in knowledge-intensive domains like healthcare. This study introduces LEAF: Learning and Evaluation Augmented by Fact-Checking, a novel approach designed to enhance the factual reliability of LLMs, with a focus on medical question answering (QA). LEAF utilizes a dual strategy to enhance the factual accuracy of responses from models such as Llama 3 70B Instruct and Llama 3 8B Instruct. The first strategy, Fact-Check","authors_text":"Hieu Tran, Junda Wang, Terrence Chen, Weijing Huang, Yujan Ting","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-31T00:18:05Z","title":"LEAF: Learning and Evaluation Augmented by Fact-Checking to Improve Factualness in Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.23526","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:33b2c77012a852f24f7a40f5bf804e2905bd1633c78e4eb688cd46101ecaa100","target":"record","created_at":"2026-07-05T09:29:01Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"d47ebbb7fa67a00b1c3693b68a6562c610935ca6353c2f567a5300d1e4f49cc0","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-31T00:18:05Z","title_canon_sha256":"a01fa8a99817eb38684468df83728cbecbbfdd60fc7d61b39249dc4d808fc141"},"schema_version":"1.0","source":{"id":"2410.23526","kind":"arxiv","version":1}},"canonical_sha256":"4f827f5018c074d51883da7823f1c897e0ec355b5e5b4b3f283d16c8304407a7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4f827f5018c074d51883da7823f1c897e0ec355b5e5b4b3f283d16c8304407a7","first_computed_at":"2026-07-05T09:29:01.579105Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:29:01.579105Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"rNspmmLXLlTPoj/dSiZG51YFLHATf1+hkf7rMietztvxmmzZYeKdMsDyoAoDLa16Jv+WAFknv7YDZ7U5bZDIDg==","signature_status":"signed_v1","signed_at":"2026-07-05T09:29:01.579641Z","signed_message":"canonical_sha256_bytes"},"source_id":"2410.23526","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:33b2c77012a852f24f7a40f5bf804e2905bd1633c78e4eb688cd46101ecaa100","sha256:82440d64ef91bf5240defc20944b7236478f6d8f072b423a874ac2e139077868"],"state_sha256":"794ca0f99bbe1f5c01b59161659f55995b188ad10222c30653ce718edba0f4f6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tfi5DsxpjzmPhDNuTFguS0kYnBvu1YWZam1975vM5Q3AKlcJhxdWCwUz1V7PJW+QRsTFEVv3S8IKmTyPUqxnBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T00:50:51.029295Z","bundle_sha256":"4f9fbaa78518367d2d2ec994de5f2e4c3a162cbf2e6661f384b1b168bab178e1"}}