{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:TO6HEQDAYPZ6I65BCDT6AHM56N","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":"d9c7171841b1b8bfd91af8d29e88eb8a09fa98fee5f97c0bb317a6ad24928b5b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-06-16T16:56:32Z","title_canon_sha256":"7e33ac2c07e3e4952f72e2477f0112a76b910763d0011ffbfcc27746f68ee8e9"},"schema_version":"1.0","source":{"id":"2306.09968","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.09968","created_at":"2026-07-05T06:21:26Z"},{"alias_kind":"arxiv_version","alias_value":"2306.09968v1","created_at":"2026-07-05T06:21:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.09968","created_at":"2026-07-05T06:21:26Z"},{"alias_kind":"pith_short_12","alias_value":"TO6HEQDAYPZ6","created_at":"2026-07-05T06:21:26Z"},{"alias_kind":"pith_short_16","alias_value":"TO6HEQDAYPZ6I65B","created_at":"2026-07-05T06:21:26Z"},{"alias_kind":"pith_short_8","alias_value":"TO6HEQDA","created_at":"2026-07-05T06:21:26Z"}],"graph_snapshots":[{"event_id":"sha256:2526ae7c5edbbf17cf9ace58a98b72b75ebac086645f8196f6cd378017e11582","target":"graph","created_at":"2026-07-05T06:21:26Z","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/2306.09968/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Large language models have exhibited exceptional performance on various Natural Language Processing (NLP) tasks, leveraging techniques such as the pre-training, and instruction fine-tuning. Despite these advances, their effectiveness in medical applications is limited, due to challenges such as factual inaccuracies, reasoning abilities, and lack grounding in real-world experience. In this study, we present ClinicalGPT, a language model explicitly designed and optimized for clinical scenarios. By incorporating extensive and diverse real-world data, such as medical records, domain-specific knowl","authors_text":"Guangyu Wang, Guoxing Yang, Longjun Fan, Xiaohu Li, Zongxin Du","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-06-16T16:56:32Z","title":"ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data and Comprehensive Evaluation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.09968","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":"sha256:084ca78cb755f5463830f45156affc6d03b7d7334cd0c2da1e4524838085ac54","target":"record","created_at":"2026-07-05T06:21:26Z","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":"d9c7171841b1b8bfd91af8d29e88eb8a09fa98fee5f97c0bb317a6ad24928b5b","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2023-06-16T16:56:32Z","title_canon_sha256":"7e33ac2c07e3e4952f72e2477f0112a76b910763d0011ffbfcc27746f68ee8e9"},"schema_version":"1.0","source":{"id":"2306.09968","kind":"arxiv","version":1}},"canonical_sha256":"9bbc724060c3f3e47ba110e7e01d9df367a79d9e810709c9867b9c8e688d3ba3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9bbc724060c3f3e47ba110e7e01d9df367a79d9e810709c9867b9c8e688d3ba3","first_computed_at":"2026-07-05T06:21:26.022925Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:21:26.022925Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jE1WVVHHuPFoWUwesicFe3nQlxK/7QVcqQoe8Buqv6Yb4Zd8OHoRlC0x97BT479fjscSxWNMRX3UwMpsCyomBw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:21:26.023367Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.09968","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:084ca78cb755f5463830f45156affc6d03b7d7334cd0c2da1e4524838085ac54","sha256:2526ae7c5edbbf17cf9ace58a98b72b75ebac086645f8196f6cd378017e11582"],"state_sha256":"f1e8396d1f4b99c89494fd9a42cc2cc178bdbd165598307c384f37481ef49f06"}