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        <title>Confidence Calibration in Large Language Model-Based Entity Matching</title>
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        <namePart type="given">Floris</namePart>
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    <name type="personal">
        <namePart type="given">Tsegaye</namePart>
        <namePart type="family">Misikir Tashu</namePart>
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    <name type="personal">
        <namePart type="given">Matias</namePart>
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    <abstract>This research aims to explore the intersection of Large Language Models and confidence calibration in Entity Matching. To this end, we perform an empirical study to compare baseline RoBERTa confidences for an Entity Matching task against confidences that are calibrated using Temperature Scaling, Monte Carlo Dropout and Ensembles. We use the Abt-Buy, DBLP-ACM, iTunes-Amazon and Company datasets. The findings indicate that the proposed modified RoBERTa model exhibits a slight overconfidence, with Expected Calibration Error scores ranging from 0.0043 to 0.0552 across datasets. We find that this overconfidence can be mitigated using Temperature Scaling, reducing Expected Calibration Error scores by up to 23.83%.</abstract>
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%0 Conference Proceedings
%T Confidence Calibration in Large Language Model-Based Entity Matching
%A Kamsteeg, Iris
%A Cardenas-Cartagena, Juan
%A van Beers, Floris
%A Misikir Tashu, Tsegaye
%A Valdenegro-Toro, Matias
%Y Noidea, Noidea
%S Proceedings of the 2nd Workshop on Uncertainty-Aware NLP (UncertaiNLP 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-349-4
%F kamsteeg-etal-2025-confidence
%X This research aims to explore the intersection of Large Language Models and confidence calibration in Entity Matching. To this end, we perform an empirical study to compare baseline RoBERTa confidences for an Entity Matching task against confidences that are calibrated using Temperature Scaling, Monte Carlo Dropout and Ensembles. We use the Abt-Buy, DBLP-ACM, iTunes-Amazon and Company datasets. The findings indicate that the proposed modified RoBERTa model exhibits a slight overconfidence, with Expected Calibration Error scores ranging from 0.0043 to 0.0552 across datasets. We find that this overconfidence can be mitigated using Temperature Scaling, reducing Expected Calibration Error scores by up to 23.83%.
%U https://aclanthology.org/2025.uncertainlp-main.12/
%P 120-137

Markdown (Informal)

[Confidence Calibration in Large Language Model-Based Entity Matching](https://aclanthology.org/2025.uncertainlp-main.12/) (Kamsteeg et al., UncertaiNLP 2025)

• Confidence Calibration in Large Language Model-Based Entity Matching (Kamsteeg et al., UncertaiNLP 2025)

ACL

• Iris Kamsteeg, Juan Cardenas-Cartagena, Floris van Beers, Tsegaye Misikir Tashu, and Matias Valdenegro-Toro. 2025. Confidence Calibration in Large Language Model-Based Entity Matching. In Proceedings of the 2nd Workshop on Uncertainty-Aware NLP (UncertaiNLP 2025), pages 120–137, Suzhou, China. Association for Computational Linguistics.