@inproceedings{dias-alexiou-etal-2026-definition,
title = "Definition Modeling for Mathematics: Understanding Audience Bias and its Problem Solving Relevance",
author = "Dias-Alexiou, Carolina and
Marrese-Taylor, Edison and
Solano, A. and
Matsuo, Yutaka",
editor = "Valentino, Marco and
Ferreira, Deborah and
Thayaparan, Mokanarangan and
Ranaldi, Leonardo and
Cheng, Jo-Ku and
Freitas, Andre",
booktitle = "The Proceedings of the 4th Workshop on Mathematical Natural Language Processing ({M}ath{NLP} 2026)",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.mathnlp-main.22/",
pages = "291--304",
ISBN = "979-8-89176-468-2",
abstract = "Definition modeling is used as a probe of what a language model has internalized about a domain, and it has been studied on general lexicography and on jargon-heavy specialist domains. We extend this task to mathematics, where a term can be defined very differently for different communities. In this context we study the baseline capacity of models to retrieve generate definitions, and their audience-bias when explaining mathematical terms without specific guidance. We further asses to what extent can they follow a certain style when directed, and to which degree being exposed to definitions aid or hurt model performance when solving mathematical questions."
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<abstract>Definition modeling is used as a probe of what a language model has internalized about a domain, and it has been studied on general lexicography and on jargon-heavy specialist domains. We extend this task to mathematics, where a term can be defined very differently for different communities. In this context we study the baseline capacity of models to retrieve generate definitions, and their audience-bias when explaining mathematical terms without specific guidance. We further asses to what extent can they follow a certain style when directed, and to which degree being exposed to definitions aid or hurt model performance when solving mathematical questions.</abstract>
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%0 Conference Proceedings
%T Definition Modeling for Mathematics: Understanding Audience Bias and its Problem Solving Relevance
%A Dias-Alexiou, Carolina
%A Marrese-Taylor, Edison
%A Solano, A.
%A Matsuo, Yutaka
%Y Valentino, Marco
%Y Ferreira, Deborah
%Y Thayaparan, Mokanarangan
%Y Ranaldi, Leonardo
%Y Cheng, Jo-Ku
%Y Freitas, Andre
%S The Proceedings of the 4th Workshop on Mathematical Natural Language Processing (MathNLP 2026)
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-468-2
%F dias-alexiou-etal-2026-definition
%X Definition modeling is used as a probe of what a language model has internalized about a domain, and it has been studied on general lexicography and on jargon-heavy specialist domains. We extend this task to mathematics, where a term can be defined very differently for different communities. In this context we study the baseline capacity of models to retrieve generate definitions, and their audience-bias when explaining mathematical terms without specific guidance. We further asses to what extent can they follow a certain style when directed, and to which degree being exposed to definitions aid or hurt model performance when solving mathematical questions.
%U https://aclanthology.org/2026.mathnlp-main.22/
%P 291-304
Markdown (Informal)
[Definition Modeling for Mathematics: Understanding Audience Bias and its Problem Solving Relevance](https://aclanthology.org/2026.mathnlp-main.22/) (Dias-Alexiou et al., MathNLP 2026)
ACL