{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:7PVVL2CKTWRYERJ6MZ4HYFJ4R7","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":"1060463b642a643b1b7b2028acbcd097d45f5b84d0a8523a9b31d20121b03d6e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-24T01:46:07Z","title_canon_sha256":"8782c78eca71a738c503b3bc1ebae48c6a5e68445b51b650c27952b561b36f0d"},"schema_version":"1.0","source":{"id":"2305.14623","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.14623","created_at":"2026-07-05T08:02:43Z"},{"alias_kind":"arxiv_version","alias_value":"2305.14623v2","created_at":"2026-07-05T08:02:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.14623","created_at":"2026-07-05T08:02:43Z"},{"alias_kind":"pith_short_12","alias_value":"7PVVL2CKTWRY","created_at":"2026-07-05T08:02:43Z"},{"alias_kind":"pith_short_16","alias_value":"7PVVL2CKTWRYERJ6","created_at":"2026-07-05T08:02:43Z"},{"alias_kind":"pith_short_8","alias_value":"7PVVL2CK","created_at":"2026-07-05T08:02:43Z"}],"graph_snapshots":[{"event_id":"sha256:cfe40c6a372cc531c5d4893123c120559a69c6518e5443eeee3d379ec2324475","target":"graph","created_at":"2026-07-05T08:02:43Z","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/2305.14623/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Fact-checking is an essential task in NLP that is commonly utilized for validating the factual accuracy of claims. Prior work has mainly focused on fine-tuning pre-trained languages models on specific datasets, which can be computationally intensive and time-consuming. With the rapid development of large language models (LLMs), such as ChatGPT and GPT-3, researchers are now exploring their in-context learning capabilities for a wide range of tasks. In this paper, we aim to assess the capacity of LLMs for fact-checking by introducing Self-Checker, a framework comprising a set of plug-and-play m","authors_text":"Baolin Peng, Jianfeng Gao, Miaoran Li, Michel Galley, Zhu Zhang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-24T01:46:07Z","title":"Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.14623","kind":"arxiv","version":2},"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:ec28bba4bee8a712c6286bd660e3eaccc06cc3cc1f750ecd299ac83d489d0a78","target":"record","created_at":"2026-07-05T08:02:43Z","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":"1060463b642a643b1b7b2028acbcd097d45f5b84d0a8523a9b31d20121b03d6e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-05-24T01:46:07Z","title_canon_sha256":"8782c78eca71a738c503b3bc1ebae48c6a5e68445b51b650c27952b561b36f0d"},"schema_version":"1.0","source":{"id":"2305.14623","kind":"arxiv","version":2}},"canonical_sha256":"fbeb55e84a9da382453e66787c153c8fff0af397bdda8fd699e8f6ba19681e23","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fbeb55e84a9da382453e66787c153c8fff0af397bdda8fd699e8f6ba19681e23","first_computed_at":"2026-07-05T08:02:43.727026Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:02:43.727026Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7iiTX4oXZn5Dkf96hfccNKgJ8V7C5/CL6kNZiQJKpZKRbH4ZamGfPhSCX2REoFLcgr2dwQT/u+sV5GlSGvbCAg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:02:43.727572Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.14623","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ec28bba4bee8a712c6286bd660e3eaccc06cc3cc1f750ecd299ac83d489d0a78","sha256:cfe40c6a372cc531c5d4893123c120559a69c6518e5443eeee3d379ec2324475"],"state_sha256":"8ca2e62fbd545e4e42f6317e06a220aaf216e7755268ef8029e63a1db041148e"}