Enhancing RAG With Active Learning on Conversation Records: Reject Incapables and Answer Capables

Retrieval-augmented generation (RAG) is a key technique for leveraging external knowledge and enhancing the factual accuracy of large language models (LLMs). However, RAG still faces challenges in ensuring fully reliable responses in all scenarios. To address this, it is essential to identify samples that tend to lead to unreliable outputs or guide LLMs toward factually 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 generated from widespread LLM usage to build these datasets, with the goal of training LLMs to appropriately handle queries outside its capabilities while providing accurate responses to manageable ones. Given the impracticality of having experts annotate all conversation records, we introduce AL4RAG, a framework that uses active learning to select the most suitable conversation samples for annotation, filtering out redundant or noisy interactions and thereby optimizing model performance within a limited 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.