用对话记录主动学习，让大模型学会拒绝胡说、准确回答。
Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capables
- 从海量对话中筛选关键样本，用主动学习减少人工标注量。
- 在多个指标上优于基线，显著降低幻觉率。
- 专为RAG设计距离度量，更适合知识增强场景。
检索增强生成（RAG）是利用外部知识、减少大语言模型（LLM）幻觉的关键技术。然而，RAG仍难以完全避免错误回答。为此，需识别易产生幻觉的样本或引导模型正确响应，专家需标注这些样本以构建高质量数据集用于优化模型。但此类数据集日益稀缺，构建困难。本文提出利用广泛使用大模型产生的大量对话记录构建数据集，训练模型避开幻觉高风险问题，同时准确回应可处理的问题。鉴于专家标注所有对话不现实，本文提出AL4RAG，通过主动学习选择最适合作标注的对话样本，在有限标注预算下优化性能。此外，针对传统主动学习方法因距离度量不适配导致与RAG不兼容的问题，开发了适用于RAG的新型样本距离测量方法。大量实验表明，该方法在多项指标上持续优于基线。
原文摘要 · Abstract (English)
Retrieval-augmented generation (RAG) is a key technique for leveraging external knowledge and reducing hallucinations in large language models (LLMs). However, RAG still struggles to fully prevent hallucinated responses. To address this, it is essential to identify samples prone to hallucination or guide LLMs toward correct responses, which experts then annotate to develop high-quality datasets for refining LLMs. However, the growing scarcity of such datasets makes their creation challenging. This paper proposes using the vast amount of conversations from widespread LLM usage to build these datasets, training LLMs to avoid hallucination-prone questions while accurately responding to manageable ones. Given the impracticality of expert-annotating all conversation records, the paper introduces AL4RAG, which uses active learning to select the most suitable conversation samples for annotation, optimizing performance within an annotation budget. Additionally, recognizing that traditional active learning methods are not fully compatible with RAG due to unsuitable distance metrics, we develop a novel sample distance measurement for RAG active learning. Extensive experiments show that our method consistently outperforms baselines across multiple metrics.
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