arXiv AI
Jul 23

Rethinking Uncertainty Evaluation in Large Language Models

arXiv:2607. 19367v1 Announce Type: new Abstract: Calibration is the primary criterion for evaluating LLM confidence, but it is insufficient: it admits trivially incoherent estimators, depends on the evaluation distribution, and does not test the extent to which the estimation can be interpreted as a consistent, underlying probability function.

By Krish Matta, Atharv Naphade, Andy Zou
arXiv Computation and Language
6d ago

A Calibrated Reflection Approach for Enhancing Confidence Estimation in LLMs

The paper introduces a Calibrated Reflection approach to improve confidence estimation in Large Language Models (LLMs). It combines structured reasoning with a distance‑aware calibration technique, featuring a Maximum Confidence Selection method, a reflection‑based prompting mechanism, and an ordinal‑aware calibration strategy. Experiments on datasets such as HelpSteer2, Llama T‑REx, and a proprietary conversational set show the method works for both conversational and fact‑based classification tasks.

By Umesh Bodhwani, Yuan Ling, Shujing Dong, Yarong Feng, Hongfei Li, Ayush Goyal
arXiv Computation and Language
Sep 1

Beyond the Final Layer: Intermediate Representations for Better Multilingual Calibration in Large Language Models

The paper investigates multilingual confidence calibration in large language models, revealing that non‑English languages are systematically less well calibrated than English. By analyzing internal representations, the authors find that late‑intermediate layers provide a more reliable confidence signal than the final layer, which is biased by English‑centric training. They propose training‑free methods such as Language‑Aware Confidence Ensemble (LACE) to adaptively select optimal layers per language, aiming to improve global equity and trustworthiness of LLMs.

By Ej Zhou, Caiqi Zhang, Tiancheng Hu, Chengzu Li, Nigel Collier, Ivan Vuli\'c, Anna Korhonen