bibtext: @inproceedings{
  kamoi2024evaluating,
  title={Evaluating {LLM}s at Detecting Errors in {LLM} Responses},
  author={Ryo Kamoi and Sarkar Snigdha Sarathi Das and Renze Lou and Jihyun Janice Ahn and Yilun Zhao and Xiaoxin Lu and Nan Zhang and Yusen Zhang and Haoran Ranran Zhang and Sujeeth Reddy Vummanthala and Salika Dave and Shaobo Qin and Arman Cohan and Wenpeng Yin and Rui Zhang},
  booktitle={First Conference on Language Modeling},
  year={2024},
  url={https://openreview.net/forum?id=dnwRScljXr}
  }
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 first error detection benchmark consisting of objective, realistic, and diverse errors made by LLMs. ReaLMistake contains three challenging and meaningful tasks that introduce objectively assessable errors in four categories (reasoning correctness, instruction-following, context-faithfulness, and parameterized knowledge), eliciting naturally observed and diverse errors in responses of GPT-4 and Llama 2 70B annotated by experts. We use ReaLMistake to evaluate error detectors based on 12 LLMs. Our findings show: 1) Top LLMs like GPT-4 and Claude 3 detect errors made by LLMs at very low recall, and all LLM-based error detectors perform much worse than humans. 2) Explanations by LLM-based error detectors lack reliability. 3) LLMs-based error detection is sensitive to small changes in prompts but remains challenging to improve. 4) Popular approaches to improving LLMs, including self-consistency and majority vote, do not improve the error detection performance. Our benchmark and code are provided at https://github.com/psunlpgroup/ReaLMistake.
title: Evaluating LLMs at Detecting Errors in LLM Responses
authors: Ryo Kamoi Sarkar Snigdha Sarathi Das Renze Lou Jihyun Janice Ahn Yilun Zhao Xiaoxin Lu Nan Zhang Yusen Zhang Haoran Ranran Zhang Sujeeth Reddy Vummanthala Salika Dave Shaobo Qin Arman Cohan Wenpeng Yin Rui Zhang