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Evaluating LLMs at Detecting Errors in LLM Responses

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Benchmark to evaluate LLMs' ability to detect errors in their own responses.

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About Evaluating LLMs at Detecting Errors in LLM Responses

ReaLMistake is the first benchmark designed to evaluate the ability of large language models (LLMs) to detect errors in their own responses. It comprises three challenging tasks covering four distinct error categories: reasoning correctness, instruction-following, context-faithfulness, and parameterized knowledge. The benchmark uses objectively assessable errors naturally occurring in GPT-4 and Llama 2 70B outputs, annotated by experts. The paper evaluates 12 LLM-based error detectors and finds that even top models like GPT-4 and Claude 3 achieve very low recall, perform much worse than humans, produce unreliable explanations, are sensitive to prompt changes, and are not improved by self-consistency or majority vote. The benchmark and associated code are publicly available.

Key Features

Objective, realistic, and diverse error types from real LLM outputs
Four error categories: reasoning correctness, instruction-following, context-faithfulness, parameterized knowledge
Three challenging tasks with expert-annotated errors
Evaluation framework for 12 LLM-based error detectors
Publicly available benchmark and code (COLM 2024)

Pros & Cons

Pros
  • First benchmark focusing on objective, realistic, and diverse LLM errors
  • Covers multiple error types that are practically relevant
  • Uses expert annotations for high-quality ground truth
  • Provides code and benchmark for reproducibility
  • Published at COLM 2024, indicating academic rigor
Cons
  • Current LLM error detectors perform much worse than humans
  • Even top models like GPT-4 and Claude 3 have very low recall
  • LLMs produce unreliable explanations for detected errors
  • Performance is sensitive to small prompt changes
  • Popular techniques like self-consistency and majority vote do not improve detection

Best For

Evaluating the error detection capability of LLMsBenchmarking new LLM error detection methodsResearch into LLM reliability and self-correctionImproving trustworthiness of LLM-generated content

FAQ

What is ReaLMistake?
ReaLMistake is a benchmark designed to evaluate the ability of LLMs to detect errors in their own responses. It consists of three tasks with four error categories: reasoning correctness, instruction-following, context-faithfulness, and parameterized knowledge.
What errors does ReaLMistake cover?
It covers errors in reasoning correctness, instruction-following, context-faithfulness, and parameterized knowledge, all objectively assessable and naturally occurring in GPT-4 and Llama 2 70B outputs.
How were the errors in the benchmark created?
Errors were elicited from GPT-4 and Llama 2 70B responses and then annotated by human experts to ensure high-quality ground truth.
What are the main findings of the paper?
Top LLMs like GPT-4 and Claude 3 detect errors at very low recall, all LLM-based error detectors perform much worse than humans, explanations are unreliable, performance is sensitive to prompt changes, and popular improvement methods like self-consistency and majority vote do not help.
Is the benchmark available for use?
Yes, the benchmark and associated code are publicly available at the URL provided in the paper.