arXiv:2605.23909v1 Announce Type: cross 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.
The rapid deployment and increasing reliance on large language models make understanding their inherent biases and limitations, particularly regarding confidence, critically important right now.
This research provides crucial insights into a fundamental limitation of current LLMs, which impacts their reliability and the trust users can place in their outputs across various applications.
Our understanding of LLM confidence is refined, highlighting that current models are systematically overconfident on hard tasks and surprisingly underconfident on easy tasks, similar to human cognitive biases.
- · AI researchers focusing on calibration
- · Developers building robust AI systems
- · Companies offering AI safety and alignment solutions
- · Uncalibrated LLM deployments
- · Applications relying solely on LLMs' self-assessed confidence
- · Users unaware of LLM confidence biases
Demand will grow for better calibration techniques and evaluation benchmarks for AI models.
New techniques will emerge to adjust or express LLM confidence more accurately, leading to more trustworthy AI applications.
The development of truly 'human-like' AI may require models to understand and express uncertainty with similar nuance, influencing the design of future emotional or cognitive AI architectures.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG