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September 5, 2025Applied SciencesOpen Access

ACR: Adaptive Confidence Re-Scoring for Reliable Answer Selection Among Multiple Candidates

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ACR improves answer accuracy while significantly reducing inference cost in large language models.
It reduces the number of inference calls by up to 95% compared to existing verification methods.
The method enhances inference efficiency, yielding accuracy gains per inference call of 2× to 17×.
ACR addresses vulnerabilities in LLMs by adaptively evaluating candidate answers for more reliable selection.

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Experiments demonstrate ACR improves answer accuracy and reduces inference cost in LLMs, suggesting a robust solution to consistency issues.

Jeong et al. (2025) studied this question.

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Experiments demonstrate ACR improves answer accuracy and reduces inference cost in LLMs, suggesting a robust solution to consistency issues.