AGENTIC-SYSTEMSMay 26, 20261 min read10 views

Confidence Calibration in Large Language Models

A new study reveals that current large language models (LLMs) tend to be overconfident in their answers, much like humans. This tendency is heavily influenced by task difficulty, leading to overconfidence in hard tasks and underconfidence in easy ones, evaluated using a new tool called LifeEval.

Computer Science > Artificial Intelligence

Title:Confidence Calibration in Large Language Models

View PDF HTML (experimental)Abstract:We investigate the calibration of large language models' (LLMs') confidence across diverse tasks. The results of our preregistered study show that the current crop of LLMs are, like people, too sure they are right: confidence exceeds accuracy, on average. Importantly, however, this tendency is moderated by a powerful hard-easy effect, wherein overconfidence is greatest on difficult tests; by contrast, easy tests actually show substantial underconfidence. We develop LifeEval, a test for evaluating model calibration across levels of difficulty.

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Source: arXiv cs.AI Recent

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