arXiv:2606.11512v1 Announce Type: new Abstract: Large language models increasingly express uncertainty through natural-language statements, yet these expressions often fail to reflect the model's sampled behavior. We study verbal uncertainty alignment as a distributional calibration problem: the appropriate uncertainty target for a prompt should be estimated from repeated model outputs rather than from an isolated response. However, group rollouts alone are insufficient, since the resulting target must provide a useful training signal. Existing targets only partially satisfy this requirement.
The proliferation of Large Language Models (LLMs) and their deployment in critical applications necessitates improved reliability and trustworthiness, making uncertainty alignment a current imperative.
Improving how LLMs express and align their internal uncertainty with verbal statements is crucial for their responsible and effective integration into decision-making processes.
This research suggests a new approach to calibrate LLM uncertainty expressions, moving from isolated responses to distributional analysis of multiple outputs, which could lead to more trustworthy AI systems.
- · AI developers
- · Trustworthy AI platforms
- · Industries relying on AI predictions
- · AI models with poor uncertainty calibration
Improved uncertainty alignment enhances the trustworthiness and utility of large language models.
More reliable AI outputs could accelerate automation and decision support in sensitive domains like finance or healthcare.
Enhanced AI explainability through better uncertainty communication could mitigate regulatory concerns around 'black box' AI.
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Read at arXiv cs.CL