Related papers: Second Guess: Detecting Uncertainty Through Abstention and Answer Stability in Small Language Models

Second Guess: Detecting Uncertainty Through Abstention and Answer Stability in Small Language Models

URL: http://arxiv.org/abs/2605.25394v1
Date: Mon, 25 May 2026 03:38:54 GMT
Title: Second Guess: Detecting Uncertainty Through Abstention and Answer Stability in Small Language Models
Abstract summary: Large language models often generate confident but incorrect answers rather than abstaining when uncertain.<n>We propose _Second Guess_, a lightweight, parameter-free prompting technique for abstention in multiple-choice question answering (MCQA)<n>Second Guess achieves the highest composite risk improvement of 10.81%.
Score: 2.5782420501870296
License: http://creativecommons.org/licenses/by/4.0/
Abstract: Large language models often generate confident but incorrect answers rather than abstaining when uncertain. This problem is particularly acute for small language models (SLMs), where computational constraints and autonomous operation amplify the need for reliable uncertainty detection. We propose _Second Guess_, a lightweight, parameter-free prompting technique for abstention in multiple-choice question answering (MCQA) that is well-suited for SLMs. Our key empirical insight is that models which truly know an answer will select it consistently, while uncertain models exhibit unstable behavior when an ``I don't know'' option is added. Evaluated on four open models (2B-8B parameters) and four benchmarks, Second Guess achieves the highest composite risk improvement of 10.81\%. Notably, it maintains an 8\% composite risk improvement on fine-tuned models where entropy-based methods degrade, and improves most for lower-performing models. All code and results required to reproduce this work is available in https://github.com/Mystic-Slice/second-guess

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