Confidence Calibration in Large Language Models
Quick Take
Large language models exhibit overconfidence, particularly on difficult tasks, necessitating better calibration methods.
Key Points
- Current LLMs' confidence often exceeds accuracy.
- Overconfidence is highest on difficult tests.
- LifeEval is introduced for model calibration assessment.
Article Excerpt
From source RSS / original summaryarXiv:2605. 23909v1 Announce Type: new 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.
Reader Mode unavailable (could not extract clean content).
Want this in your inbox every morning?
Daily brief at your local 8am — bilingual EN/中文, free.
More from arXiv cs.AI
See more →From Prompts to Protocols: An AI Agent for Laboratory Automation
An AI agent integrates large language models for automating laboratory protocols, enhancing efficiency and accuracy.