HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models
This addresses the issue of unreliable outputs in LLMs for users relying on factual accuracy, though it is incremental as it builds on existing evaluation methods.
The authors tackled the problem of hallucinations in large language models by introducing HaluEval, a large-scale benchmark for evaluating hallucination recognition, finding that ChatGPT generates hallucinated content in about 19.5% of responses and that existing models struggle with recognition.
Large language models (LLMs), such as ChatGPT, are prone to generate hallucinations, i.e., content that conflicts with the source or cannot be verified by the factual knowledge. To understand what types of content and to which extent LLMs are apt to hallucinate, we introduce the Hallucination Evaluation benchmark for Large Language Models (HaluEval), a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing hallucination. To generate these samples, we propose a ChatGPT-based two-step framework, i.e., sampling-then-filtering. Besides, we also hire some human labelers to annotate the hallucinations in ChatGPT responses. The empirical results suggest that ChatGPT is likely to generate hallucinated content in specific topics by fabricating unverifiable information (i.e., about $19.5\%$ responses). Moreover, existing LLMs face great challenges in recognizing the hallucinations in texts. However, our experiments also prove that providing external knowledge or adding reasoning steps can help LLMs recognize hallucinations. Our benchmark can be accessed at https://github.com/RUCAIBox/HaluEval.