Language Model Introspection

Language model introspection asks what models can know about their own computations, learned behavior, uncertainty, and likely outputs, and whether that knowledge can support auditing or safer decisions. Researchers use activation probes and logit lenses, causal interventions and attention-head analysis, behavioral benchmarks, and self-report training methods such as adapters and reinforcement learning. Tests often find that internal states contain useful signals, but models may not be able to report them reliably; surface cues and task shortcuts can also make apparent introspection hard to distinguish from ordinary prediction. A central open question is when a model has genuine access to information about its own computation, rather than merely producing a plausible report, and how such access varies across tasks and settings.