C-FAITH: A Chinese Fine-Grained Benchmark for Automated Hallucination Evaluation

• URL: http://arxiv.org/abs/2504.10167v1
• Date: Mon, 14 Apr 2025 12:21:55 GMT
• Title: C-FAITH: A Chinese Fine-Grained Benchmark for Automated Hallucination Evaluation
• Authors: Xu Zhang, Zhifei Liu, Jiahao Wang, Huixuan Zhang, Fan Xu, Junzhe Zhang, Xiaojun Wan,
• Abstract summary: We introduce HaluAgent, an agentic framework that automatically constructs fine-grained QA dataset based on some knowledge documents.<n>Our experiments demonstrate that the manually designed rules and prompt optimization can improve the quality of generated data.
• Score: 58.40263551616771
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the rapid advancement of large language models, they remain highly susceptible to generating hallucinations, which significantly hinders their widespread application. Hallucination research requires dynamic and fine-grained evaluation. However, most existing hallucination benchmarks (especially in Chinese language) rely on human annotations, making automatical and cost-effective hallucination evaluation challenging. To address this, we introduce HaluAgent, an agentic framework that automatically constructs fine-grained QA dataset based on some knowledge documents. Our experiments demonstrate that the manually designed rules and prompt optimization can improve the quality of generated data. Using HaluAgent, we construct C-FAITH, a Chinese QA hallucination benchmark created from 1,399 knowledge documents obtained from web scraping, totaling 60,702 entries. We comprehensively evaluate 16 mainstream LLMs with our proposed C-FAITH, providing detailed experimental results and analysis.

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