GrACE: A Generative Approach to Better Confidence Elicitation and Efficient Test-Time Scaling in Large Language Models
Year: 2026
Type: conference-paper
Abstract: Assessing the reliability of Large Language Models (LLMs) by confidence elicitation is a prominent approach to AI safety in highstakes applications, such as healthcare and finance.Existing methods either require expensive computational overhead or suffer from poor calibration, making them impractical and unreliable for real-world deployment.In this work, we propose GrACE, a Generative Approach to Confidence Elicitation that enables scalable and reliable confidence elicitation for LLMs.GrACE adopts a novel mechanism in which the model expresses confidence by the similarity between the last hidden state and the embedding of a special token appended to the vocabulary, in real-time.We fine-tune the model for calibrating the confidence with targets associated with accuracy.Extensive experiments show that the confidence produced by GrACE achieves the best discriminative capacity and calibration on open-ended generation tasks without resorting to additional sampling or an auxiliary model.Moreover, we propose two confidence-based strategies for test-time scaling with GrACE, which not only improve the accuracy of the final decision but also significantly reduce the number of required samples, highlighting its potential as a practical solution for deploying LLMs with reliable, on-the-fly confidence estimation.The code is available at: https://github.com/petezone/Grace.
Authors: Zhaohan Zhang, Ziquan Liu, Ioannis Patras
Institution: Queen Mary University of London
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W7166827828