IgnitionInnovators at "Discharge Me!": Chain-of-Thought Instruction Finetuning Large Language Models for Discharge Summaries
<|reference_start|>IgnitionInnovators at "Discharge Me!": Chain-of-Thought Instruction Finetuning Large Language Models for Discharge Summaries: 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<|reference_end|>
@article{tang2024ignitioninnovators,
    title={IgnitionInnovators at "Discharge Me!": Chain-of-Thought Instruction
  Finetuning Large Language Models for Discharge Summaries},
    author={An Quang Tang, Xiuzhen Zhang, Minh Ngoc Dinh},
    journal={arXiv preprint arXiv:2407.17636},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.17636},
    primaryClass={cs.CL}
}