LLMs Should Express Uncertainty Explicitly

📰 ArXiv cs.AI

LLMs should be trained to express uncertainty explicitly to improve decision-making in applications like abstention and verification

advanced Published 8 Apr 2026

Action Steps

Train LLMs to verbalize calibrated confidence scores
Implement a global interface for uncertainty expression
Develop a local interface for uncertainty expression at the token level
Evaluate the effectiveness of explicit uncertainty expression in downstream tasks

Who Needs to Know This

AI engineers and researchers benefit from this approach as it enables more transparent and controllable models, while product managers can utilize this feature to make more informed decisions

Key Insight

💡 Explicit uncertainty expression can improve the transparency and controllability of LLMs

Key Takeaways

LLMs should be trained to express uncertainty explicitly to improve decision-making in applications like abstention and verification

Full Article

Title: LLMs Should Express Uncertainty Explicitly

Abstract:
arXiv:2604.05306v1 Announce Type: cross Abstract: Large language models are increasingly used in settings where uncertainty must drive decisions such as abstention, retrieval, and verification. Most existing methods treat uncertainty as a latent quantity to estimate after generation rather than a signal the model is trained to express. We instead study uncertainty as an interface for control. We compare two complementary interfaces: a global interface, where the model verbalizes a calibrated con

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