Second Guess: Detecting Uncertainty Through Abstention and Answer Stability in Small Language Models
The paper titled 'Second Guess' introduces a method for detecting uncertainty in small language models through a technique that encourages abstention. This approach addresses the issue of small language models providing confident but incorrect answers when uncertain. The proposed method shows significant improvements in risk assessment, particularly for lower-performing models.
- ▪The technique is designed for small language models, which face unique challenges due to computational constraints.
- ▪'Second Guess' achieves a composite risk improvement of 10.81% across various benchmarks.
- ▪The method maintains an 8% improvement on fine-tuned models where traditional entropy-based methods fail.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.25394 (cs) [Submitted on 25 May 2026] Title:Second Guess: Detecting Uncertainty Through Abstention and Answer Stability in Small Language Models Authors:Ashwath Vaithinathan Aravindan, Mayank Kejriwal View a PDF of the paper titled Second Guess: Detecting Uncertainty Through Abstention and Answer Stability in Small Language Models, by Ashwath Vaithinathan Aravindan and 1 other authors View PDF HTML (experimental) 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.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
