Causal Evidence that Language Models use Confidence to Drive Behavior
Metacognition -- assessing the quality of one's own cognitive performance -- guides adaptive behavior across species. Substantial research demonstrates that confidence signals can be extracted from language model outputs, yet a fundamental question remains: do models actually use these signals to control behavior, such as deciding whether to answer or abstain? To investigate, we developed a four-phase paradigm. Phase~1 elicited baseline confidence estimates without an abstention option. Phase~2 revealed that LLMs apply an implicit threshold to internal confidence when deciding to abstain, with confidence effect sizes approximately an order of magnitude larger than alternative mechanisms. ...
| Authors | Dharshan Kumaran, Nathaniel Daw, Simon Osindero et al. |
|---|---|
| Primary category | cs.LG |
| Published | 23 March 2026 |
| Last updated at source | 18 September 2026 |
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