Bi'an: A Bilingual Benchmark and Model for Hallucination Detection in Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2502.19209v1
• Date: Wed, 26 Feb 2025 15:12:59 GMT
• Title: Bi'an: A Bilingual Benchmark and Model for Hallucination Detection in Retrieval-Augmented Generation
• Authors: Zhouyu Jiang, Mengshu Sun, Zhiqiang Zhang, Lei Liang,
• Abstract summary: We introduce bftextBi'an, a novel framework featuring a bilingual benchmark dataset and lightweight judge models.<n>The dataset supports rigorous evaluation across multiple RAG scenarios, while the judge models are fine-tuned from compact open-source LLMs.
• Score: 6.549143816134529
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) effectively reduces hallucinations in Large Language Models (LLMs) but can still produce inconsistent or unsupported content. Although LLM-as-a-Judge is widely used for RAG hallucination detection due to its implementation simplicity, it faces two main challenges: the absence of comprehensive evaluation benchmarks and the lack of domain-optimized judge models. To bridge these gaps, we introduce \textbf{Bi'an}, a novel framework featuring a bilingual benchmark dataset and lightweight judge models. The dataset supports rigorous evaluation across multiple RAG scenarios, while the judge models are fine-tuned from compact open-source LLMs. Extensive experimental evaluations on Bi'anBench show our 14B model outperforms baseline models with over five times larger parameter scales and rivals state-of-the-art closed-source LLMs. We will release our data and models soon at https://github.com/OpenSPG/KAG.

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