How do LLMs Compute Verbal Confidence

📰 ArXiv cs.AI

Researchers investigate how LLMs compute verbal confidence, exploring when confidence is calculated and what it represents

advanced Published 1 Apr 2026

Action Steps

Investigate the internal mechanisms of LLMs to determine when verbal confidence is computed
Analyze the relationship between answer generation and confidence calculation to understand if confidence is calculated just-in-time or cached
Examine the representation of verbal confidence to determine what it signifies in the context of LLMs
Evaluate the implications of verbal confidence computation on model performance and uncertainty estimation

Who Needs to Know This

AI engineers and researchers benefit from understanding how LLMs generate uncertainty estimates, which can inform model development and improvement

Key Insight

💡 Verbal confidence computation in LLMs is not well understood, but research can uncover its internal mechanisms and representation

Key Takeaways

Researchers investigate how LLMs compute verbal confidence, exploring when confidence is calculated and what it represents

Full Article

Title: How do LLMs Compute Verbal Confidence

Abstract:
arXiv:2603.17839v2 Announce Type: replace-cross Abstract: Verbal confidence -- prompting LLMs to state their confidence as a number or category -- is widely used to extract uncertainty estimates from black-box models. However, how LLMs internally generate such scores remains unknown. We address two questions: first, when confidence is computed - just-in-time when requested, or automatically during answer generation and cached for later retrieval; and second, what verbal confidence represents - t

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