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

• URL: http://arxiv.org/abs/2510.02377v1
• Date: Tue, 30 Sep 2025 01:25:19 GMT
• Title: Uncertainty-Aware Answer Selection for Improved Reasoning in Multi-LLM Systems
• Authors: Aakriti Agrawal, Rohith Aralikatti, Anirudh Satheesh, Souradip Chakraborty, Amrit Singh Bedi, Furong Huang,
• Abstract summary: Large Language Models (LLMs) have demonstrated exceptional capabilities, yet selecting the most reliable response from multiple LLMs remains a challenge.<n>Existing approaches often depend on costly external verifiers, human evaluators, or self-consistency techniques that require multiple samples from a single model.<n>We propose a principled, novel and computationally efficient method to select the best response from multiple different LLMs using a calibrated log-likelihood score.
• Score: 55.6590601898194
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) have demonstrated exceptional capabilities, yet selecting the most reliable response from multiple LLMs remains a challenge, particularly in resource-constrained settings. Existing approaches often depend on costly external verifiers, human evaluators, or self-consistency techniques that require multiple samples from a single model. While multi-LLM systems produce more diverse responses than single models and thus have greater potential, they often underperform compared to single LLM self-consistency. We propose a principled, novel and computationally efficient method to select the best response from multiple different LLMs using a calibrated log-likelihood score, implicitly leveraging the inherent knowledge and confidence of these models. Our method demonstrates improvements of approx. 4%, 3%, and 5% across both debate (multi-round LLM discussions) and non-debate (Best-of-N with multiple LLMs) settings on GSM8K, MMLU (6 subsets), and ARC datasets respectively.

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