The research introduces ReaLMistake, a benchmark for error detection in LLM responses across realistic and diverse scenarios. The study examines errors in the categories of reasoning correctness, instruction-following, and context-faithfulness, using responses annotated by experts from models like GPT-4 and Llama 2 70B.

Key Points:

• Error detection was evaluated using 12 LLMs, revealing low recall rates in error detection by leading LLMs.
• LLM-based error detectors showed poor performance as compared to human evaluation.
• The reliability of explanations provided by LLM detectors is questionable.
• Sensitivity to prompt changes was found, but enhancements in LLM-based error detection remain challenging.
• Techniques like self-consistency and majority vote do not significantly enhance error detection proficiency.

The research at ReaLMistake is critical as it reveals the limitations of current LLMs in recognizing their own mishaps—an essential step toward creating more reliable and authoritative LLMs for various applications from education to information curation.