Large language models (LLMs), such as ChatGPT, are prone to generate hallucinations, \ie 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 for Large Language Models (HELMA) benchmark, a large collection of generated and human-annotated hallucinated samples for evaluating the performance of LLMs in recognizing and alleviating hallucination. To generate these samples, we propose a ChatGPT-based two-step framework, \ie sampling-then-filtering. Specifically, we first adopt two different sampling methods to generate hallucinated samples based on instructions, and then use an example-enhanced filtering method to select the best one. Furthermore, we also hire some human labelers to annotate the hallucinations in ChatGPT responses. The empirical results suggest that ChatGPT has some probabilities to generate hallucinations and existing LLMs face great challenges in recognizing the hallucinations in text. In addition, the performance can be improved by providing external knowledge or adding reasoning steps. Our benchmark can be accessed at https://github.com/RUCAIBox/HELMA.

本研究引入了 Hallucination Evaluation for Large Language Models（HELMA）基准来评估 LLM 的幻觉表现，并提出了基于 ChatGPT 的取样-过滤框架来生成大规模的，人类标注的幻觉数据集，并指出 ChatGPT 生成幻觉的概率较大，现有的 LLM 在识别文本幻觉方面面临巨大挑战，但可通过提供外部知识或添加推理步骤来改善表现。

HELMA: 面向大型语言模型的大规模幻想评估基准测试