Evaluating the Error Detection Capabilities of Large Language Models

Large language models (LLMs) often make mistakes in their responses, and detecting these errors is crucial for their real-world applications. However, little research has been conducted on error detection for LLM responses due to the lack of suitable benchmarks. This work introduces ReaLMistake, the first benchmark for evaluating error detection methods on objective, realistic, and diverse errors made by LLMs.