Language Model Self-Assessment

Language model self-assessment asks whether a model can judge its own answer quality, uncertainty, capabilities, errors, and behavior—and use those judgments to decide when to answer, abstain, verify, or revise. Methods range from verbal confidence, token probabilities, and agreement across samples to hidden-state probes, structured self-questioning, and training with reinforcement learning or self-generated rubrics. Work evaluates these signals on factual and specialized question answering, math and code reasoning, safety tasks, and agent interactions, measuring calibration, selective risk, and whether reflection improves outcomes. Self-assessment can support routing and refinement, but may be sensitive to prompts, miss shared errors, and fail to track objective correctness or actual behavior, making external or environment-grounded checks important.