{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:Z2B3R4SH5HVFIA4TTOKF5NPD74","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":"dbea89137f30935d95f86e9824a26b0f9b8f0a1201178ccefb3605b8ec58a569","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-08-28T08:06:33Z","title_canon_sha256":"2d79f508671134c3548c648bdaa3ebde6cccb0fd433b8cda131e20f197a801f6"},"schema_version":"1.0","source":{"id":"2508.20525","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2508.20525","created_at":"2026-07-05T12:00:54Z"},{"alias_kind":"arxiv_version","alias_value":"2508.20525v1","created_at":"2026-07-05T12:00:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.20525","created_at":"2026-07-05T12:00:54Z"},{"alias_kind":"pith_short_12","alias_value":"Z2B3R4SH5HVF","created_at":"2026-07-05T12:00:54Z"},{"alias_kind":"pith_short_16","alias_value":"Z2B3R4SH5HVFIA4T","created_at":"2026-07-05T12:00:54Z"},{"alias_kind":"pith_short_8","alias_value":"Z2B3R4SH","created_at":"2026-07-05T12:00:54Z"}],"graph_snapshots":[{"event_id":"sha256:07504f549ff6b06e2498c750ef552d1306d8fd70f6ad606c6bd10e73fb607955","target":"graph","created_at":"2026-07-05T12:00:54Z","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/2508.20525/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Fact-checking for health-related content is challenging due to the limited availability of annotated training data. In this study, we propose a synthetic data generation pipeline that leverages large language models (LLMs) to augment training data for health-related fact checking. In this pipeline, we summarize source documents, decompose the summaries into atomic facts, and use an LLM to construct sentence-fact entailment tables. From the entailment relations in the table, we further generate synthetic text-claim pairs with binary veracity labels. These synthetic data are then combined with t","authors_text":"Jiahe Qian, Jingze Zhang, Yifan Peng, Yiliang Zhou","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-08-28T08:06:33Z","title":"Enhancing Health Fact-Checking with LLM-Generated Synthetic Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.20525","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:c115d5b1026b0deffab3a2db21338b9625cf99e2a30b4c0451e28cee2b2e3294","target":"record","created_at":"2026-07-05T12:00:54Z","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":"dbea89137f30935d95f86e9824a26b0f9b8f0a1201178ccefb3605b8ec58a569","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.AI","submitted_at":"2025-08-28T08:06:33Z","title_canon_sha256":"2d79f508671134c3548c648bdaa3ebde6cccb0fd433b8cda131e20f197a801f6"},"schema_version":"1.0","source":{"id":"2508.20525","kind":"arxiv","version":1}},"canonical_sha256":"ce83b8f247e9ea5403939b945eb5e3ff11710037c61ae7c3cf3e0fbfc49bb904","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ce83b8f247e9ea5403939b945eb5e3ff11710037c61ae7c3cf3e0fbfc49bb904","first_computed_at":"2026-07-05T12:00:54.229219Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:00:54.229219Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"AA8zqTjC7Vl/MrgZ7Z3OyPQYS4yheHlWgmrlrDNmiHj2pXxmiXgZZndu6WAUKyxGdSzcsF8un5nxlB8UH+S3AA==","signature_status":"signed_v1","signed_at":"2026-07-05T12:00:54.229721Z","signed_message":"canonical_sha256_bytes"},"source_id":"2508.20525","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c115d5b1026b0deffab3a2db21338b9625cf99e2a30b4c0451e28cee2b2e3294","sha256:07504f549ff6b06e2498c750ef552d1306d8fd70f6ad606c6bd10e73fb607955"],"state_sha256":"30a879ac46c2fccf74f5578d60897df6716e85121ffb70f8ce3f198b15ee1a18"}