Evaluating LLMs at Detecting Errors in LLM Responses

• This paper tackles the challenge of identifying errors in responses generated by Large Language Models (LLMs), presenting the ReaLMistake benchmark for error detection.

• Conducting error annotations for LLM responses is complex, owing to the subjective nature of many NLP tasks, which prompted the creation of ReaLMistake.

• ReaLMistake poses objectively assessable errors across reasoning correctness, instruction-following, context-faithfulness, and parameterized knowledge categories within responses of GPT-4 and Llama 2 70B.

• A total of 12 different LLMs were benchmarked using ReaLMistake, revealing that even top LLMs struggle with error detection, significantly lagging behind human performance.

• Benchmark Details: The ReaLMistake offers diverse, realistic, and objective errors for comprehensive evaluation.

• LLM Performance: The study shows that top-tier models like GPT-4 and Claude 3 have low recall in error detection.

• Error Detector Reliability: The reliability of error detectors based on LLMs is brought into question.

• Error Detection Sensitivity: Sensitivity to minor prompt alterations hampers the ability to enhance error detection.

It’s clear that error detection in LLM responses is a domain needing urgent attention. The development of ReaLMistake is crucial for advancing the reliability of LLMs and opens the door to more resilient AI systems capable of introspective accuracy assessment.

Explore the error detection study