<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tang-etal-2024-ignitioninnovators">
    <titleInfo>
        <title>IgnitionInnovators at “Discharge Me!”: Chain-of-Thought Instruction Finetuning Large Language Models for Discharge Summaries</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">An</namePart>
        <namePart type="given">Quang</namePart>
        <namePart type="family">Tang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xiuzhen</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Minh</namePart>
        <namePart type="given">Ngoc</namePart>
        <namePart type="family">Dinh</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2024-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 23rd Workshop on Biomedical Natural Language Processing</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Dina</namePart>
            <namePart type="family">Demner-Fushman</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Sophia</namePart>
            <namePart type="family">Ananiadou</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Makoto</namePart>
            <namePart type="family">Miwa</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Kirk</namePart>
            <namePart type="family">Roberts</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Junichi</namePart>
            <namePart type="family">Tsujii</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Bangkok, Thailand</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>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://anonymous.4open.science/r/Discharge_LLM-A233</abstract>
    <identifier type="citekey">tang-etal-2024-ignitioninnovators</identifier>
    <location>
        <url>https://aclanthology.org/2024.bionlp-1.65</url>
    </location>
    <part>
        <date>2024-08</date>
        <extent unit="page">
            <start>731</start>
            <end>739</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T IgnitionInnovators at “Discharge Me!”: Chain-of-Thought Instruction Finetuning Large Language Models for Discharge Summaries
%A Tang, An Quang
%A Zhang, Xiuzhen
%A Dinh, Minh Ngoc
%Y Demner-Fushman, Dina
%Y Ananiadou, Sophia
%Y Miwa, Makoto
%Y Roberts, Kirk
%Y Tsujii, Junichi
%S Proceedings of the 23rd Workshop on Biomedical Natural Language Processing
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F tang-etal-2024-ignitioninnovators
%X 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://anonymous.4open.science/r/Discharge_LLM-A233
%U https://aclanthology.org/2024.bionlp-1.65
%P 731-739

Markdown (Informal)

[IgnitionInnovators at “Discharge Me!”: Chain-of-Thought Instruction Finetuning Large Language Models for Discharge Summaries](https://aclanthology.org/2024.bionlp-1.65) (Tang et al., BioNLP-WS 2024)

• IgnitionInnovators at “Discharge Me!”: Chain-of-Thought Instruction Finetuning Large Language Models for Discharge Summaries (Tang et al., BioNLP-WS 2024)

ACL

• An Quang Tang, Xiuzhen Zhang, and Minh Ngoc Dinh. 2024. IgnitionInnovators at “Discharge Me!”: Chain-of-Thought Instruction Finetuning Large Language Models for Discharge Summaries. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 731–739, Bangkok, Thailand. Association for Computational Linguistics.