<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="zhang-etal-2026-fine">
    <titleInfo>
        <title>Fine-Tuning Large Language Models for Codebook-Guided Coding of Students’ Mathematics Metaphor Responses</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Liang</namePart>
        <namePart type="family">Zhang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Stephen</namePart>
        <namePart type="family">Hwang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yue</namePart>
        <namePart type="family">Ma</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Jinfa</namePart>
        <namePart type="family">Cai</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Joshua</namePart>
            <namePart type="family">Wilson</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Christopher</namePart>
            <namePart type="family">Ormerod</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Magdalen</namePart>
            <namePart type="family">Beiting-Parrish</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>National Council on Measurement in Education (NCME)</publisher>
            <place>
                <placeTerm type="text">Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-9983004-0-0</identifier>
    </relatedItem>
    <abstract>Student-generated mathematics metaphors reveal students’ attitudes and beliefs but are costly to code manually. We evaluate LoRA-based fine-tuning of compact open-weight LLMs for valence-intensity and thematic coding. Fine-tuning substantially improves coding performance and reliability, making these models competitive with proprietary prompt-only LLMs while supporting local, privacy-conscious deployment.</abstract>
    <identifier type="citekey">zhang-etal-2026-fine</identifier>
    <location>
        <url>https://aclanthology.org/2026.aimecon-main.48/</url>
    </location>
    <part>
        <date>2026-10</date>
        <extent unit="page">
            <start>433</start>
            <end>442</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Fine-Tuning Large Language Models for Codebook-Guided Coding of Students’ Mathematics Metaphor Responses
%A Zhang, Liang
%A Hwang, Stephen
%A Ma, Yue
%A Cai, Jinfa
%Y Wilson, Joshua
%Y Ormerod, Christopher
%Y Beiting-Parrish, Magdalen
%S Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers
%D 2026
%8 October
%I National Council on Measurement in Education (NCME)
%C Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States
%@ 979-8-9983004-0-0
%F zhang-etal-2026-fine
%X Student-generated mathematics metaphors reveal students’ attitudes and beliefs but are costly to code manually. We evaluate LoRA-based fine-tuning of compact open-weight LLMs for valence-intensity and thematic coding. Fine-tuning substantially improves coding performance and reliability, making these models competitive with proprietary prompt-only LLMs while supporting local, privacy-conscious deployment.
%U https://aclanthology.org/2026.aimecon-main.48/
%P 433-442

Markdown (Informal)

[Fine-Tuning Large Language Models for Codebook-Guided Coding of Students’ Mathematics Metaphor Responses](https://aclanthology.org/2026.aimecon-main.48/) (Zhang et al., AIME-Con 2026)

• Fine-Tuning Large Language Models for Codebook-Guided Coding of Students’ Mathematics Metaphor Responses (Zhang et al., AIME-Con 2026)

ACL

• Liang Zhang, Stephen Hwang, Yue Ma, and Jinfa Cai. 2026. Fine-Tuning Large Language Models for Codebook-Guided Coding of Students’ Mathematics Metaphor Responses. In Proceedings of the Artificial Intelligence in Measurement and Education Conference (AIME-Con): Full Papers, pages 433–442, Wyndham Grand Pittsburgh Downtown, Pittsburgh, Pennsylvania, United States. National Council on Measurement in Education (NCME).