Retrieval-Augmented Generation (RAG) systems have gained widespread adoption by application builders because they leverage sources of truth to enable Large Language Models (LLMs) to generate more factually sound responses. However, hallucinations, instances of LLM responses that are unfaithful to the provided context, often prevent these systems from being deployed in production environments. Current hallucination detection methods typically involve human evaluation or the use of closed-source models to review RAG system outputs for hallucinations. Both human evaluators and closed-source models suffer from scaling issues due to their high costs and slow inference speeds. In this work, we introduce a perturbed multi-hop QA dataset with induced hallucinations. Via supervised fine-tuning on our dataset, we achieve better recall with a 7B model than GPT-4o on the RAGTruth hallucination detection benchmark and offer competitive performance on precision and accuracy, all while using a fraction of the parameters. Code is released at our repository.

本研究针对当前RAG系统中幻觉检测方法成本高、速度慢的问题，提出了一种轻量级的开源幻觉检测系统Osiris。通过在一个引入幻觉的多跳问答数据集上进行监督微调，Osiris在RAGTruth幻觉检测基准上表现出比GPT-4o更好的召回率，并在精确度和准确性上也达到了竞争水平，使用的参数量却大大减少。 

Osiris：轻量级开源幻觉检测系统