Second Guess: Detecting Uncertainty Through Abstention and Answer Stability in Small Language Models
arXiv:2605.25394v1 Announce Type: cross 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,
As small language models become more prevalent for autonomous operations, the need for reliable uncertainty detection is amplified, particularly given their computational constraints.
Improving the ability of SLMs to detect and signal their uncertainty directly addresses a critical safety and reliability challenge for AI applications, preventing confident but incorrect outputs.
The introduction of techniques like 'Second Guess' can lead to more trustworthy and deployable SLMs, reducing the risks associated with AI errors in sensitive applications.
- · Small Language Model developers
- · AI safety researchers
- · Autonomous system manufacturers
- · Edge computing providers
- · Platforms reliant on unquestioning AI decisions
SLMs integrated into critical systems will be able to 'abstain' rather than provide erroneous answers.
Increased adoption of SLMs in environments where reliability and safety are paramount, such as healthcare or industrial control.
The development of a trust framework for AI beyond correctness, including mechanisms for AI to communicate its own confidence levels.
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Read at arXiv cs.CL