{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:L6MVUO7VTJ3TOKIETAJJVKF5IN","short_pith_number":"pith:L6MVUO7V","schema_version":"1.0","canonical_sha256":"5f995a3bf59a7737290498129aa8bd436c75b1e8780c9ca6ecf0c186dbd09d46","source":{"kind":"arxiv","id":"2412.04512","version":1},"attestation_state":"computed","paper":{"title":"Prompting Large Language Models for Clinical Temporal Relation Extraction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Cui Tao, Degui Zhi, Evan Yu, Haifang Li, Jianfu Li, Jianping He, Laila Rasmy, Zenan Sun","submitted_at":"2024-12-04T18:35:28Z","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"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2412.04512","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-12-04T18:35:28Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"e978c5bddfe0a9aab41136f0074e13c4c9d933fe1e6ed8799080b4e395513a44","abstract_canon_sha256":"2569dec404346a09cdf70a934e43381306897176a81e90984fab6b58885a49ad"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:45:18.917945Z","signature_b64":"0xhnT992Q8O5vhrqldUVDl5xGAt7Puv74ZQT4g717/jiqyOlrDdMAMi9cPTY9ylZBcXLHmLUCK3rUtg8mz8RBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5f995a3bf59a7737290498129aa8bd436c75b1e8780c9ca6ecf0c186dbd09d46","last_reissued_at":"2026-07-05T09:45:18.917440Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:45:18.917440Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Prompting Large Language Models for Clinical Temporal Relation Extraction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CL","authors_text":"Cui Tao, Degui Zhi, Evan Yu, Haifang Li, Jianfu Li, Jianping He, Laila Rasmy, Zenan Sun","submitted_at":"2024-12-04T18:35:28Z","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"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.04512","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/2412.04512/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2412.04512","created_at":"2026-07-05T09:45:18.917505+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.04512v1","created_at":"2026-07-05T09:45:18.917505+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.04512","created_at":"2026-07-05T09:45:18.917505+00:00"},{"alias_kind":"pith_short_12","alias_value":"L6MVUO7VTJ3T","created_at":"2026-07-05T09:45:18.917505+00:00"},{"alias_kind":"pith_short_16","alias_value":"L6MVUO7VTJ3TOKIE","created_at":"2026-07-05T09:45:18.917505+00:00"},{"alias_kind":"pith_short_8","alias_value":"L6MVUO7V","created_at":"2026-07-05T09:45:18.917505+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2608.12779","citing_title":"CRAFT: LLM-Based Iterative Refinement for Temporal Reasoning over Clinical Narratives","ref_index":31,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/L6MVUO7VTJ3TOKIETAJJVKF5IN","json":"https://pith.science/pith/L6MVUO7VTJ3TOKIETAJJVKF5IN.json","graph_json":"https://pith.science/api/pith-number/L6MVUO7VTJ3TOKIETAJJVKF5IN/graph.json","events_json":"https://pith.science/api/pith-number/L6MVUO7VTJ3TOKIETAJJVKF5IN/events.json","paper":"https://pith.science/paper/L6MVUO7V"},"agent_actions":{"view_html":"https://pith.science/pith/L6MVUO7VTJ3TOKIETAJJVKF5IN","download_json":"https://pith.science/pith/L6MVUO7VTJ3TOKIETAJJVKF5IN.json","view_paper":"https://pith.science/paper/L6MVUO7V","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.04512&json=true","fetch_graph":"https://pith.science/api/pith-number/L6MVUO7VTJ3TOKIETAJJVKF5IN/graph.json","fetch_events":"https://pith.science/api/pith-number/L6MVUO7VTJ3TOKIETAJJVKF5IN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/L6MVUO7VTJ3TOKIETAJJVKF5IN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/L6MVUO7VTJ3TOKIETAJJVKF5IN/action/storage_attestation","attest_author":"https://pith.science/pith/L6MVUO7VTJ3TOKIETAJJVKF5IN/action/author_attestation","sign_citation":"https://pith.science/pith/L6MVUO7VTJ3TOKIETAJJVKF5IN/action/citation_signature","submit_replication":"https://pith.science/pith/L6MVUO7VTJ3TOKIETAJJVKF5IN/action/replication_record"}},"created_at":"2026-07-05T09:45:18.917505+00:00","updated_at":"2026-07-05T09:45:18.917505+00:00"}