arXiv Computation and Language
Sep 18

An Analysis of Training-Free Self-Reported Confidence in Language Models

The paper investigates whether language models’ self-reported confidence is meaningful without additional training. By evaluating three training‑free signals—direct verbalization, post‑hoc probability estimates, and agreement across multiple generations—on 100 TriviaQA questions, the authors find that direct verbalization alone achieves high AUROC scores (0.956 and 0.937) for correctness prediction, while agreement-based methods perform noticeably worse. Re‑eliciting confidence for the same answers shows modest score shifts and occasional decision flips, and an audit of biography claims reveals only a small confidence gap between supported and contradicted statements.

By Lukas Meyer, Sofia Rossi, Wei Chen, Thomas Laurent, Yiming Li
arXiv Machine Learning
Oct 2

Also Small Models Can Reasonably Self-Evaluate Their Confidence

The paper evaluates how language models of different sizes self-assess their confidence on question‑answering tasks across general and specialized domains. It finds that while accuracy drops for smaller models and more specialized tasks, the reliability of self‑evaluated confidence signals remains largely stable. Consequently, even less capable models can provide reasonable confidence estimates, making them suitable for resource‑constrained applications.

By Idil Kapikiran, Thomas Decker, Thomas Runkler