IgnitionInnovators在"Discharge Me!"任务中：基于思维链指令微调大型语言模型生成出院摘要
IgnitionInnovators at "Discharge Me!": Chain-of-Thought Instruction Finetuning Large Language Models for Discharge Summaries
AI总结：
本文提出基于LLM的框架解决出院摘要文档记录任务，通过指令微调与思维链提示策略优化文本生成，有效提升了临床信息的结构正确性与忠实度。
摘要：
本文提出了我们针对Discharge Me!共享任务的解决方案，该任务与第23届生物医学自然语言处理研讨会（BioNLP）同期举办。在本研究中，我们开发了一个基于LLM的框架来解决出院摘要文档记录（DSD）任务，即生成出院摘要中的两个关键目标部分：“简要住院病程”和“出院指导”。通过简化近期在LLM上的指令微调过程，我们探索了几种提示策略，以将LLM最佳地适配到DSD的特定生成任务中。实验结果表明，提供清晰的输出结构，辅以一套全面的思维链问题，能有效提升模型的推理能力，从而增强生成文本中临床信息的结构正确性和忠实度。源代码可在以下地址获取：https://github.com/antangrocket1312/Discharge_LLM
英文摘要：
This paper presents our proposed approach to the Discharge Me! shared task, collocated with the 23th Workshop on Biomedical Natural Language Processing (BioNLP). In this work, we develop an LLM-based framework for solving the Discharge Summary Documentation (DSD) task, i.e., generating the two critical target sections `Brief Hospital Course' and `Discharge Instructions' in the discharge summary. By streamlining the recent instruction-finetuning process on LLMs, we explore several prompting strategies for optimally adapting LLMs to specific generation task of DSD. Experimental results show that providing a clear output structure, complimented by a set of comprehensive Chain-of-Thoughts (CoT) questions, effectively improves the model's reasoning capability, and thereby, enhancing the structural correctness and faithfulness of clinical information in the generated text. Source code is available at: https://github.com/antangrocket1312/Discharge_LLM
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
