UHGE val: Benchmarking the Hallucination of C hinese Large Language Models via Unconstrained Generation (2024)

Abstract: Large language models (LLMs) produce hallucinated text, compromising their practical utility in professional contexts. To assess the reliability of LLMs, numerous initiatives have developed benchmark evaluations for hallucination phenomena. However, they often employ constrained generation techniques to produce the evaluation dataset due to cost and time limitations. For instance, this may involve employing directed hallucination induction or deliberately modifying authentic text to generate hallucinations. These are not congruent with the unrestricted text generation demanded by real-world applications. Furthermore, a well-established Chinese-language dataset dedicated to the evaluation of hallucinations is presently lacking. Consequently, we have developed an Unconstrained Hallucination Generation Evaluation (UHGEval) benchmark, containing hallucinations generated by LLMs with minimal restrictions. Concurrently, we have established a comprehensive benchmark evaluation framework to aid subsequent researchers in undertaking scalable and reproducible experiments. We have also evaluated prominent Chinese LLMs and the GPT series models to derive insights regarding hallucination.

## introduction large language models (llms) have unparalleled proficiency in language generation, knowledge application, and intricate reasoning @xcite . however, they invariably manifest hallucination @xcite , as they often generate content that is incongruent with user input, the model's output context, or factual information. real-world hallucination examples from our uhgeval dataset can be observed in fig. 1 . the motie in south korea korea aerospace industries stated that the south korean government will continue to advance this export plan. during the holiday, the national highway passenger traffic reached 250 310 million person-times, representing a year-on-year increase of 8.9% 3.2%. sickle cell disease is a severe hereditary blood disorder that can lead to atherosclerosis anemia, infarction, and other complications. the fabricated news content depicted in fig. 1 offers no utility to journalists; on the contrary, the verification and rectification of such content exacts a toll on the valuable time of journalists. to this concern, it is crucial to first formulate a comprehensive, stringent, and demanding benchmark for the assessment of hallucination in language generation @xcite . while there have been a bunch of efforts to develop benchmarks for hallucination assessment, they always employ restricted techniques to produce particular kinds of hallucinated utterances. this approach is at odds with real-world scenarios where hallucinations arise in unrestricted, spontaneously generated content. for example, halueval specifies the type of hallucination in the prompt when generating hallucinated text: "you are trying to answer a question but misunderstand the question context and intention" @xcite . additionally, benchmarks such as hades annotate hallucinations at a finer granularity by generating token-level hallucinations based on text perturbations @xcite , but the text perturbation method is still constrained. hallucinations must be generated in an unconstrained setting; otherwise, it's difficult to determine whether the hallucinated texts in many ## related work this section outlines hallucination evaluation benchmarks, their characteristics, and evaluation methodologies. a summary of these benchmarks is presented in ## conclusion llms are rapidly evolving, heralding a new era of potential applications within the realm of professional content generation. the progression of llms in this domain necessitates the establishment of robust benchmarks to steer their development effectively. in this work, we introduce a novel hallucination benchmark dataset using an unconstrained fashion, encompassing more than 5,000 instances annotated at the keyword level. additionally, we propose a secure, scalable, and user-friendly evaluation framework to facilitate comprehensive assessments. through meticulous experimentation on eleven prominent llms, our study has unearthed a series of enlightening findings. looking ahead, our research endeavors will persist in exploring the intricacies of hallucination phenomena within professional content generation, aiming to further understand and enhance llm capabilities.