Reasoning Models Better Express Their Confidence
Dongkeun Yoon, Seungone Kim, Sohee Yang, Sunkyoung Kim, Soyeon Kim, Yongil Kim, Eunbi Choi, Yireun Kim, Minjoon Seo
2025-05-21
Summary
This paper talks about how AI models that explain their thought process step-by-step are better at showing how sure they are about their answers compared to models that just give answers without much explanation.
What's the problem?
The problem is that many AI models can't reliably tell us how confident they are in their answers, which makes it hard for people to know when to trust them, especially on tricky questions.
What's the solution?
To solve this, the researchers looked at reasoning models that use a chain-of-thought approach, meaning they break down their thinking into steps. These models can adjust their confidence as they work through a problem, making their answers more trustworthy and clear.
Why it matters?
This matters because it helps people know when to trust AI and when to double-check its answers, making AI tools safer and more useful in real life.
Abstract
Reasoning models, which perform chain-of-thought reasoning, exhibit superior confidence calibration compared to non-reasoning models by dynamically adjusting their confidence during the reasoning process.