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        <title>ChulaNLP at SemEval-2026 Task 5: Regression-Calibrated LLM for Word-Sense Scoring</title>
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            <namePart type="given">Ekaterina</namePart>
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    <abstract>Word Sense Disambiguation (WSD) is typically framed as a classification task that selects one correct sense for a word. However, real language is often less clear-cut, as a homonym may support several plausible interpretations. SemEval 2026 Task 5 addresses this limitation by introducing plausibility rating, where models estimate how likely each sense is in a narrative context, aligning predictions with graded human judgments. We use GlossBERT and BEM as encoder-based baselines and show that large language models (LLMs) produce more accurate plausibility estimates. Building on this observation, we propose a regression-calibrated LLM model that applies linear regression to adjust raw LLM outputs to better match human annotation patterns. Our calibrated model achieves the highest within-standard-deviation accuracy among our evaluated systems, demonstrating that lightweight post-hoc calibration can substantially improve LLM performance on graded semantic judgment tasks.</abstract>
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%0 Conference Proceedings
%T ChulaNLP at SemEval-2026 Task 5: Regression-Calibrated LLM for Word-Sense Scoring
%A Limsuwan, Wayu
%A Rutherford, Attapol
%Y Kochmar, Ekaterina
%Y Ghosh, Debanjan
%Y North, Kai
%Y Komachi, Mamoru
%S Proceedings of the 20th International Workshop on Semantic Evaluation (2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-414-9
%F limsuwan-rutherford-2026-chulanlp
%X Word Sense Disambiguation (WSD) is typically framed as a classification task that selects one correct sense for a word. However, real language is often less clear-cut, as a homonym may support several plausible interpretations. SemEval 2026 Task 5 addresses this limitation by introducing plausibility rating, where models estimate how likely each sense is in a narrative context, aligning predictions with graded human judgments. We use GlossBERT and BEM as encoder-based baselines and show that large language models (LLMs) produce more accurate plausibility estimates. Building on this observation, we propose a regression-calibrated LLM model that applies linear regression to adjust raw LLM outputs to better match human annotation patterns. Our calibrated model achieves the highest within-standard-deviation accuracy among our evaluated systems, demonstrating that lightweight post-hoc calibration can substantially improve LLM performance on graded semantic judgment tasks.
%U https://aclanthology.org/2026.semeval-1.343/
%P 2723-2728

Markdown (Informal)

[ChulaNLP at SemEval-2026 Task 5: Regression-Calibrated LLM for Word-Sense Scoring](https://aclanthology.org/2026.semeval-1.343/) (Limsuwan & Rutherford, SemEval 2026)

• ChulaNLP at SemEval-2026 Task 5: Regression-Calibrated LLM for Word-Sense Scoring (Limsuwan & Rutherford, SemEval 2026)

ACL

• Wayu Limsuwan and Attapol Rutherford. 2026. ChulaNLP at SemEval-2026 Task 5: Regression-Calibrated LLM for Word-Sense Scoring. In Proceedings of the 20th International Workshop on Semantic Evaluation (2026), pages 2723–2728, San Diego, California, USA. Association for Computational Linguistics.