D ia H alu: A Dialogue-level Hallucination Evaluation Benchmark for Large Language Models (2024)

Abstract: Though large language models (LLMs) achieve significant success in recent years, the hallucination issue remains a challenge, and numerous benchmarks are proposed for hallucination detection. Nevertheless, some of these benchmarks are not naturally generated by LLMs but are intentionally induced. Also, many merely focus on the factuality hallucination while ignoring the faithfulness hallucination. Additionally, although dialogue pattern is more widely utilized in the era of LLMs, current benchmarks only concentrate on sentence-level and passage-level hallucination. In this study, we propose DiaHalu, the first dedicated dialogue-level hallucination evaluation benchmark for LLMs to our knowledge. Initially, we integrate the collected topics into system prompts and facilitate a dialogue between two LLMs. Subsequently, we manually modify the contents that do not adhere to human language conventions and then have LLMs re-generate, simulating authentic human-machine interaction scenarios. Finally, professional scholars annotate all the samples in the dataset. DiaHalu covers four common multi-turn dialogue domains and five hallucination subtypes, extended from factuality and faithfulness hallucination. Experiments through some well-known LLMs and detection methods on the dataset show that DiaHalu is a challenging benchmark, holding significant value for further research.

## introduction large language models (llms) @xcite , addressing many downstream tasks through natural language generation (nlg) technique, achieve significant success across diverse fields of natural language processing (nlp) @xcite @xcite . with a considerable volume of parameters and sophisticated training methodologies, llms significantly propelled advancements in artificial intelligence @xcite . despite many advantages of llms, the issue of hallucination remains a primary concern @xcite . hallucination predominantly centers on the inclination of llms to generate nonsensical or untruthful contents for specific sources @xcite . therefore, the occurrence of hallucination poses risks to the application of llms in various real-world scenarios @xcite @xcite . given the aforementioned risks, hallucination de-tection emerges as a highly crucial task. in recent years, researchers propose numerous benchmarks for hallucination detection task @xcite @xcite . nevertheless, several problems persist in these benchmarks. (1) not naturally generated. one pitfall of existing benchmarks for detecting llms' hallucination is that the hallucinated contents are typically induced via manually designed trigger prompts @xcite , while not naturally generated by llms as in daily usage @xcite . therefore, we propose a new dialogue-level hallucination evaluation benchmark for large language models (diahalu). we initially determine four domains for multi-turn dialogue: knowledgegrounded, task-oriented, chit-chat and reasoning. for each domain, we undertake a three-step process to construct the dataset. (1) we collect topics for dialogue from various sources, incorporate the topics into artificially designed system prompts and input them into two llms, enabling them to engage in a multi-turn dialogue. (2) since the knowledgegrounded and task-oriented domains stand for real human-machine interaction scenarios, we align the contents of one of the conversational participants with human language. we manually modify the contents that do not conform to human language conventions and have llms re-generate, resulting in the final responses. @xcite professional scholars annotate all the samples with labels, hallucination subtypes and locations, as well as explanations. it is noteworthy that we not only consider the factuality hallucination but also further classify the faithfulness hallucination into three types: incoherence, irrelevance and overreliance. we similarly introduce the reasoning hallucination for the reasoning domain. the advantages of ours compared with previous benchmarks are listed in ## experiments in this section, we assess the performance of several evaluation models and specialized methods on the dataset we introduced. thereby, we can trial the effectiveness of existing methods in detecting dialogue-level hallucination. we still conduct more fine-grained detection and explore whether the phenomenon of hallucination snowballing exists. ## conclusion in this paper, we propose a dialogue-level hallucination evaluation benchmark named diahalu. we construct the benchmark in a three-step process. the diahalu covers four multi-turn dialogue domains and five hallucination subtypes. experiments through some well-known llms and specialized detection methods on the benchmark show that it is a challenging task, holding significant value for further research @xcite .