RAGTruth：用于开发可信检索增强语言模型的幻觉语料库
RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models
AI总结：
本研究构建了包含近1.8万条标注回复的RAGTruth幻觉语料库，用于RAG场景下的词级幻觉分析，基准测试了多模型幻觉频率，验证了用该语料微调小模型可达到媲美GPT-4提示方法的幻觉检测效果。
摘要：
检索增强生成（RAG）已成为缓解大语言模型（LLM）幻觉问题的主流技术。即便集成了RAG，LLM仍可能生成与检索内容无依据支撑或相矛盾的表述。为制定RAG场景下有效的幻觉抑制策略，构建可衡量幻觉程度的基准数据集至关重要。本文提出RAGTruth，这是专门用于分析大语言模型应用中标准RAG框架下、跨多领域多任务的词级幻觉的语料库。RAGTruth包含近18000条由不同大语言模型使用RAG自然生成的回复，这些回复均经过精细的人工标注，覆盖单案例层面与词级层面，还纳入了幻觉强度的评估。我们不仅对不同大语言模型的幻觉发生频率进行了基准测试，还严格评估了现有多种幻觉检测方法的有效性。此外，我们证明了利用RAGTruth这类高质量数据集，可以对规模相对较小的大语言模型进行微调，使其在幻觉检测任务上达到与使用GPT-4等最先进大语言模型的现有提示工程方法相当的竞争力水平。
英文摘要：
Retrieval-augmented generation (RAG) has become a main technique for alleviating hallucinations in large language models (LLMs). Despite the integration of RAG, LLMs may still present unsupported or contradictory claims to the retrieved contents. In order to develop effective hallucination prevention strategies under RAG, it is important to create benchmark datasets that can measure the extent of hallucination. This paper presents RAGTruth, a corpus tailored for analyzing word-level hallucinations in various domains and tasks within the standard RAG frameworks for LLM applications. RAGTruth comprises nearly 18,000 naturally generated responses from diverse LLMs using RAG. These responses have undergone meticulous manual annotations at both the individual cases and word levels, incorporating evaluations of hallucination intensity. We not only benchmark hallucination frequencies across different LLMs, but also critically assess the effectiveness of several existing hallucination detection methodologies. Furthermore, we show that using a high-quality dataset such as RAGTruth, it is possible to finetune a relatively small LLM and achieve a competitive level of performance in hallucination detection when compared to the existing prompt-based approaches using state-of-the-art large language models such as GPT-4.
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