Language Model Error Detection

Language model error detection studies how to recognize mistakes such as hallucinated facts, faulty reasoning, incorrect code or tool calls, and errors in specialized domains. Methods use output signals such as token probabilities, entropy, and agreement across samples or models, as well as internal activations, probes, retrieval, execution tests, and formal verifiers. Benchmarks span question answering, mathematical reasoning, coding, clinical and legal tasks, and multilingual settings, but results can be distorted by benchmark artifacts or fail to transfer across models and domains. A central challenge is that detecting an error does not ensure that a system can correct it: confident mistakes may be hard to observe, while interventions can introduce new errors.