H alu E val: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models (2023)

Abstract: 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 for Large Language Models (HaluEval) benchmark, 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% user queries). Moreover, existing LLMs face great challenges in recognizing the hallucinations in texts. While, our experiments also prove that the hallucination recognition can be improved by providing external knowledge or adding reasoning steps.

## introduction the advent of large language models (llms) @xcite has ushered in a paradigm shift in natural language processing (nlp), making unprecedented progress in text generation and understanding @xcite . the remarkable language ability makes llms core in a number of products with millions of users, such as the coding assistant copilot and recent chatgpt. despite these prominent capabilities of llms trained on large text corpus, recent work has shown that llms are prone to suffer from hallucination generations across various applications @xcite ## the halueval benchmark as the goal of halueval is to understand what types of content and to which extent llms tend to hallucinate, the benchmark contains a myriad of correct samples and their hallucinated counterparts. this collection is created via two ways, i.e., automatic generation and human annotation. ## conclusion we introduce halueval, a large-scale collection of generated and human-annotated hallucinated samples for evaluating the performance of llms in recognizing hallucinations. to automatically generate large-scale samples, we propose a two-step approach, i.e., sampling-then-filtering. we first introduce two different sampling methods to generate diverse samples using instructions and then filter and select the difficult one. besides, we invite qualified human labelers to annotate the hallucinations of chatgpt responses given user queries. we find that, existing llms mostly fail to recognize the hallucinations in text and tend to generate hallucinated content. finally, we suggest several strategies to help llms recognize hallucinations. our benchmark can facilitate research in understanding what types of content and to which extent llms tend to hallucinate, ultimately paving the way for building more effective and reliable llms in the future.