From Signals to Transfer: A Factorised Study of Probe-Based Uncertainty Estimation in Large Language Models
arXiv:2606.27679v1 Announce Type: cross 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. Howeve
The rapid deployment of LLMs highlights the critical need for reliable uncertainty estimation to ensure their safe and effective application, especially with increasing autonomy.
Improving the accuracy and methodology of uncertainty estimation in LLMs is crucial for building trustworthy AI systems and expanding their deployment in high-stakes environments.
Our understanding of which internal signals are most effective for robust uncertainty estimation in LLMs is becoming clearer, allowing for more targeted development of reliable AI.
- · AI Safety Researchers
- · LLM Developers
- · High-Reliability AI Applications
- · Uncertainty-Prone LLM Deployments
More reliable methods for detecting hallucinations and errors in LLMs will emerge.
Increased trust and adoption of AI systems in critical decision-making processes will become possible.
The development of highly autonomous AI agents will accelerate due to improved safety and predictability.
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Read at arXiv cs.AI