Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capables
This addresses the challenge of creating high-quality datasets to reduce hallucinations in RAG for LLM applications, but it is incremental as it builds on existing active learning and RAG techniques.
The paper tackles the problem of hallucination in retrieval-augmented generation (RAG) for large language models by proposing AL4RAG, which uses active learning on conversation records to select samples for annotation, optimizing performance within a budget; experiments show it consistently outperforms baselines across multiple metrics.
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.