November 8, 2025Open Access

Uncertainty-Aware Answer Selection for Improved Reasoning in Multi-LLM Systems

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The proposed approach improves reasoning by leveraging log-likelihood scores from multiple LLMs, and knowledge of their outputs.
Improvements of 4%, 3%, and 5% were demonstrated in both debate and non-debate settings, with key datasets being GSM8K, MMLU, and ARC.
Assessment involved assessing diverse responses in multi-LLM interactions rather than single LLM models, focusing on efficiency and accuracy.
Enhanced response selection may enable broader applications for complex decision-making tasks with LLMs.

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Analysis reveals enhanced reasoning in multi-LLM systems, suggesting a novel approach for better response selection.

Agrawal et al. (2025) studied this question.

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