HalluDial: A Large-Scale Benchmark for Automatic Dialogue-Level Hallucination Evaluation

• URL: http://arxiv.org/abs/2406.07070v1
• Date: Tue, 11 Jun 2024 08:56:18 GMT
• Title: HalluDial: A Large-Scale Benchmark for Automatic Dialogue-Level Hallucination Evaluation
• Authors: Wen Luo, Tianshu Shen, Wei Li, Guangyue Peng, Richeng Xuan, Houfeng Wang, Xi Yang,
• Abstract summary: Large Language Models (LLMs) have significantly advanced the field of Natural Language Processing (NLP) HalluDial is the first comprehensive large-scale benchmark for automatic dialogue-level hallucination evaluation. The benchmark includes 4,094 dialogues with a total of 146,856 samples.
• Score: 19.318217051269382
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have significantly advanced the field of Natural Language Processing (NLP), achieving remarkable performance across diverse tasks and enabling widespread real-world applications. However, LLMs are prone to hallucination, generating content that either conflicts with established knowledge or is unfaithful to the original sources. Existing hallucination benchmarks primarily focus on sentence- or passage-level hallucination detection, neglecting dialogue-level evaluation, hallucination localization, and rationale provision. They also predominantly target factuality hallucinations while underestimating faithfulness hallucinations, often relying on labor-intensive or non-specialized evaluators. To address these limitations, we propose HalluDial, the first comprehensive large-scale benchmark for automatic dialogue-level hallucination evaluation. HalluDial encompasses both spontaneous and induced hallucination scenarios, covering factuality and faithfulness hallucinations. The benchmark includes 4,094 dialogues with a total of 146,856 samples. Leveraging HalluDial, we conduct a comprehensive meta-evaluation of LLMs' hallucination evaluation capabilities in information-seeking dialogues and introduce a specialized judge language model, HalluJudge. The high data quality of HalluDial enables HalluJudge to achieve superior or competitive performance in hallucination evaluation, facilitating the automatic assessment of dialogue-level hallucinations in LLMs and providing valuable insights into this phenomenon. The dataset and the code are available at https://github.com/FlagOpen/HalluDial.

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