arXiv:2508.08204v2 Announce Type: replace-cross Abstract: There has been much recent interest in evaluating large language models for uncertainty calibration to facilitate model control and modulate user trust. Inference time uncertainty, which may provide a real-time signal to the model or external control modules, is particularly important for applying these concepts to improve LLM-user experience in practice. While many of the existing papers consider model calibration, comparatively little work has sought to evaluate how closely model uncertainty aligns to human uncertainty. In this work,
The rapid deployment of LLMs in user-facing applications highlights an urgent need for understanding and controlling their uncertainty to foster user trust and effective interaction.
Improving LLM uncertainty calibration, especially aligning with human understanding, is critical for real-world adoption, safety, and the development of reliable AI agents.
This research provides a framework for evaluating inference-time uncertainty in LLMs against human perception, which can lead to more robust and trustable AI systems.
- · AI developers
- · LLM application users
- · AI safety researchers
- · Trustworthy AI platforms
- · Developers of uncalibrated AI
- · Applications with high-stakes decision making relying on opaque LLMs
More accurate and interpretable uncertainty quantification in LLMs will enable their use in more sensitive domains.
Improved human-alignment of LLM uncertainty can lead to higher user adoption rates and better human-AI collaboration.
The ability of LLMs to self-assess and communicate uncertainty more effectively could accelerate the development of truly autonomous and reliable AI agents.
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Read at arXiv cs.AI