{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:L6MVUO7VTJ3TOKIETAJJVKF5IN","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":"2569dec404346a09cdf70a934e43381306897176a81e90984fab6b58885a49ad","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-04T18:35:28Z","title_canon_sha256":"e978c5bddfe0a9aab41136f0074e13c4c9d933fe1e6ed8799080b4e395513a44"},"schema_version":"1.0","source":{"id":"2412.04512","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2412.04512","created_at":"2026-07-05T09:45:18Z"},{"alias_kind":"arxiv_version","alias_value":"2412.04512v1","created_at":"2026-07-05T09:45:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.04512","created_at":"2026-07-05T09:45:18Z"},{"alias_kind":"pith_short_12","alias_value":"L6MVUO7VTJ3T","created_at":"2026-07-05T09:45:18Z"},{"alias_kind":"pith_short_16","alias_value":"L6MVUO7VTJ3TOKIE","created_at":"2026-07-05T09:45:18Z"},{"alias_kind":"pith_short_8","alias_value":"L6MVUO7V","created_at":"2026-07-05T09:45:18Z"}],"graph_snapshots":[{"event_id":"sha256:4b008e3b4c69ce92af5cdf553d3fdfc0bdfff2f0b1c5976845a7cc7068aeceae","target":"graph","created_at":"2026-07-05T09:45:18Z","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/2412.04512/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Objective: This paper aims to prompt large language models (LLMs) for clinical temporal relation extraction (CTRE) in both few-shot and fully supervised settings. Materials and Methods: This study utilizes four LLMs: Encoder-based GatorTron-Base (345M)/Large (8.9B); Decoder-based LLaMA3-8B/MeLLaMA-13B. We developed full (FFT) and parameter-efficient (PEFT) fine-tuning strategies and evaluated these strategies on the 2012 i2b2 CTRE task. We explored four fine-tuning strategies for GatorTron-Base: (1) Standard Fine-Tuning, (2) Hard-Prompting with Unfrozen LLMs, (3) Soft-Prompting with Frozen LLM","authors_text":"Cui Tao, Degui Zhi, Evan Yu, Haifang Li, Jianfu Li, Jianping He, Laila Rasmy, Zenan Sun","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-04T18:35:28Z","title":"Prompting Large Language Models for Clinical Temporal Relation Extraction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.04512","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":"sha256:7e76a6a8769066a022cfd7ad696a5c76a5699140df64322b773077266c955f80","target":"record","created_at":"2026-07-05T09:45:18Z","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":"2569dec404346a09cdf70a934e43381306897176a81e90984fab6b58885a49ad","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-04T18:35:28Z","title_canon_sha256":"e978c5bddfe0a9aab41136f0074e13c4c9d933fe1e6ed8799080b4e395513a44"},"schema_version":"1.0","source":{"id":"2412.04512","kind":"arxiv","version":1}},"canonical_sha256":"5f995a3bf59a7737290498129aa8bd436c75b1e8780c9ca6ecf0c186dbd09d46","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5f995a3bf59a7737290498129aa8bd436c75b1e8780c9ca6ecf0c186dbd09d46","first_computed_at":"2026-07-05T09:45:18.917440Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:45:18.917440Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0xhnT992Q8O5vhrqldUVDl5xGAt7Puv74ZQT4g717/jiqyOlrDdMAMi9cPTY9ylZBcXLHmLUCK3rUtg8mz8RBA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:45:18.917945Z","signed_message":"canonical_sha256_bytes"},"source_id":"2412.04512","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7e76a6a8769066a022cfd7ad696a5c76a5699140df64322b773077266c955f80","sha256:4b008e3b4c69ce92af5cdf553d3fdfc0bdfff2f0b1c5976845a7cc7068aeceae"],"state_sha256":"524620bf5233e13e838713fd50b60dd3194ac3e36ee6c736dba79ea19b66d38b"}