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        <title>Confidence as a Tie-Breaker: Reassessing Multilingual Hedging Bias in LLM-as-a-Judge Evaluation</title>
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    <abstract>LLM judges are often used to score generated answers, but their decisions may be affected by surface style rather than semantic correctness. We introduce PolyJudge-Uncertain, a controlled benchmark for studying multilingual hedging effects in LLM-as-a-judge evaluation. The benchmark contains 5,120 short factual QA instances across English, Hindi, Hinglish, and Bengali, balancing assertive versus hedged style and correct versus incorrect answers. A small pilot suggested a large pointwise penalty against hedged answers. After repairing multilingual templates and adding quality-control checks, this pointwise effect largely disappears: final pointwise accuracy is 99.8%, with no meaningful assertive-hedged gap. The robust remaining effect is pairwise: when two answers are equally correct and differ only in style, the judge prefers the assertive answer in 1,276 of 1,280 cases. We interpret this as a protocol- and task-specific assertiveness preference, not as a universal bias against hedging. Our findings highlight benchmark auditing as a central requirement for multilingual judge-bias research.</abstract>
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%0 Conference Proceedings
%T Confidence as a Tie-Breaker: Reassessing Multilingual Hedging Bias in LLM-as-a-Judge Evaluation
%A Datta, Rajashik
%A Baitalik, Sanjan
%Y T.Y.S.S., Santosh
%Y Rodriguez, Juan Diego
%Y de Gibert, Ona
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-393-7
%F datta-baitalik-2026-confidence
%X LLM judges are often used to score generated answers, but their decisions may be affected by surface style rather than semantic correctness. We introduce PolyJudge-Uncertain, a controlled benchmark for studying multilingual hedging effects in LLM-as-a-judge evaluation. The benchmark contains 5,120 short factual QA instances across English, Hindi, Hinglish, and Bengali, balancing assertive versus hedged style and correct versus incorrect answers. A small pilot suggested a large pointwise penalty against hedged answers. After repairing multilingual templates and adding quality-control checks, this pointwise effect largely disappears: final pointwise accuracy is 99.8%, with no meaningful assertive-hedged gap. The robust remaining effect is pairwise: when two answers are equally correct and differ only in style, the judge prefers the assertive answer in 1,276 of 1,280 cases. We interpret this as a protocol- and task-specific assertiveness preference, not as a universal bias against hedging. Our findings highlight benchmark auditing as a central requirement for multilingual judge-bias research.
%U https://aclanthology.org/2026.acl-srw.33/
%P 393-402

Markdown (Informal)

[Confidence as a Tie-Breaker: Reassessing Multilingual Hedging Bias in LLM-as-a-Judge Evaluation](https://aclanthology.org/2026.acl-srw.33/) (Datta & Baitalik, ACL 2026)

• Confidence as a Tie-Breaker: Reassessing Multilingual Hedging Bias in LLM-as-a-Judge Evaluation (Datta & Baitalik, ACL 2026)

ACL

• Rajashik Datta and Sanjan Baitalik. 2026. Confidence as a Tie-Breaker: Reassessing Multilingual Hedging Bias in LLM-as-a-Judge Evaluation. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 393–402, San Diego, California, United States. Association for Computational Linguistics.