Metacognition in Language Models

The field asks whether language models can tell what they know, detect errors or changes in their internal processing, and use such assessments to decide when to revise, abstain, retrieve information, or spend more computation. Studies measure confidence calibration, error detection, self-prediction, and tool-use decisions on question-answering, reasoning, and agent benchmarks; methods include verbal confidence and token probabilities, hidden-state probes and causal interventions, and reinforcement-trained controllers. Structured reflection and adaptive control can improve accuracy or efficiency in some settings, but confidence and self-reports often fail to track correctness or transfer reliably. A central open tension is whether models have usable access to their internal states, or whether apparent metacognition reflects surface cues, task shortcuts, or external scaffolding.