From Signals to Transfer: A Factorised Study of Probe-Based Uncertainty Estimation in Large Language Models

2026-06-25

AI Summary

Existing probing-based uncertainty estimation methods suffer from entangled design choices in feature representation, training data, and evaluation protocols, making it difficult to isolate the key drivers of performance improvements. This work presents the first factorized dissection of probing approaches, systematically evaluating—within a unified framework—the impact of signal types, prompting strategies, and label construction schemes. We introduce a transferable pre-trained probe that leverages hidden states and attention features, followed by structured compression and cross-task transfer training. Our analysis reveals that raw hidden states excel in in-domain settings, whereas structured features demonstrate superior robustness under distributional shift. The proposed method achieves effective transfer on open-ended fact generation tasks, establishing a stable and reliable baseline for uncertainty estimation.

Abstract

Probe-based uncertainty estimation (UE) has emerged as a prominent approach to detect hallucinations in Large Language Models (LLMs) by learning uncertainty from internal model signals. Yet, recent methods vary simultaneously across feature design, training data construction, and evaluation setting, obscuring what actually drives performance. To address this issue, we propose a factorised study of probe-based UE under matched conditions. Our results show that raw hidden states and attention features are difficult to outperform in-domain. However, under distribution shift, structured and compressed features are more robust, suggesting that in-domain performance alone is insufficient to measure progress. Furthermore, prompting and label construction significantly affect probe behaviour. Building on these best-practice findings, we train benchmark-based pretrained probes that transfer reasonably well to open-ended factual generation, providing a stable off-the-shelf baseline. Our work encourages more deployment-oriented evaluation of probe-based uncertainty estimators. The code repository is available at https://github.com/ponhvoan/ProbeUE.

Problem

• uncertainty estimation
• large language models
• probe-based methods
• hallucination detection
• distribution shift

Innovation

• probe-based uncertainty estimation
• factorised study
• distribution shift
• feature robustness
• transferable probes