Evaluating LLMs at Detecting Errors in LLM Responses

arXiv:2404.03602v1 Announce Type: new
Abstract: With Large Language Models (LLMs) being widely used across various tasks, detecting errors in their responses is increasingly crucial. However, little research has been conducted on error detection of LLM responses. Collecting error annotations on LLM responses is challenging due to the subjective nature of many NLP tasks, and thus previous research focuses on tasks of little practical value (e.g., word sorting) or limited error types (e.g., faithfulness in summarization). This work introduces ReaLMistake, the …

arxiv cs.cl errors llm llms responses type