@inproceedings{zhao-vydiswaran-2025-team,
title = "Team {NLP}4{H}ealth at {C}hemo{T}imelines 2025: Finetuning Large Language Models for Temporal Relation Extractions from Clinical Notes",
author = "Zhao, Zhe and
Vydiswaran, V.G.Vinod",
editor = "Ben Abacha, Asma and
Bethard, Steven and
Bitterman, Danielle and
Naumann, Tristan and
Roberts, Kirk",
booktitle = "Proceedings of the 7th Clinical Natural Language Processing Workshop",
month = oct,
year = "2025",
address = "Virtual",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.clinicalnlp-1.4/",
pages = "22--29",
abstract = "Extracting chemotherapy timelines from clinical narratives is a challenging task, but critical for cancer research and practice. In this paper, we present our approach and the research investigation we conducted to participate in Subtask 1 of the ChemoTimelines 2025 shared task on predicting temporal relations between pre-identified events and time expressions. We evaluated multiple fine-tuned large language models for the task. We used supervised fine-tuning strategies for Llama3-8B model to classify temporal relations. Further, we set up zero-shot prompting for Qwen3-14B to normalize time expressions. We also pre-trained and fine-tuned a Llama3-3B model using unlabeled notes and achieved results comparable with the fine-tuned Llama3-8B model. Our results demonstrate the effectiveness of fine-tuning and continual pre-training strategies in adapting large language models to domain-specific tasks."
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<abstract>Extracting chemotherapy timelines from clinical narratives is a challenging task, but critical for cancer research and practice. In this paper, we present our approach and the research investigation we conducted to participate in Subtask 1 of the ChemoTimelines 2025 shared task on predicting temporal relations between pre-identified events and time expressions. We evaluated multiple fine-tuned large language models for the task. We used supervised fine-tuning strategies for Llama3-8B model to classify temporal relations. Further, we set up zero-shot prompting for Qwen3-14B to normalize time expressions. We also pre-trained and fine-tuned a Llama3-3B model using unlabeled notes and achieved results comparable with the fine-tuned Llama3-8B model. Our results demonstrate the effectiveness of fine-tuning and continual pre-training strategies in adapting large language models to domain-specific tasks.</abstract>
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%0 Conference Proceedings
%T Team NLP4Health at ChemoTimelines 2025: Finetuning Large Language Models for Temporal Relation Extractions from Clinical Notes
%A Zhao, Zhe
%A Vydiswaran, V.G.Vinod
%Y Ben Abacha, Asma
%Y Bethard, Steven
%Y Bitterman, Danielle
%Y Naumann, Tristan
%Y Roberts, Kirk
%S Proceedings of the 7th Clinical Natural Language Processing Workshop
%D 2025
%8 October
%I Association for Computational Linguistics
%C Virtual
%F zhao-vydiswaran-2025-team
%X Extracting chemotherapy timelines from clinical narratives is a challenging task, but critical for cancer research and practice. In this paper, we present our approach and the research investigation we conducted to participate in Subtask 1 of the ChemoTimelines 2025 shared task on predicting temporal relations between pre-identified events and time expressions. We evaluated multiple fine-tuned large language models for the task. We used supervised fine-tuning strategies for Llama3-8B model to classify temporal relations. Further, we set up zero-shot prompting for Qwen3-14B to normalize time expressions. We also pre-trained and fine-tuned a Llama3-3B model using unlabeled notes and achieved results comparable with the fine-tuned Llama3-8B model. Our results demonstrate the effectiveness of fine-tuning and continual pre-training strategies in adapting large language models to domain-specific tasks.
%U https://aclanthology.org/2025.clinicalnlp-1.4/
%P 22-29
Markdown (Informal)
[Team NLP4Health at ChemoTimelines 2025: Finetuning Large Language Models for Temporal Relation Extractions from Clinical Notes](https://aclanthology.org/2025.clinicalnlp-1.4/) (Zhao & Vydiswaran, ClinicalNLP 2025)
ACL