Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check
arxiv(2024)
摘要
Retrieval-Augmented Generation (RAG) aims to generate more reliable and
accurate responses, by augmenting large language models (LLMs) with the
external vast and dynamic knowledge. Most previous work focuses on using RAG
for single-round question answering, while how to adapt RAG to the complex
conversational setting wherein the question is interdependent on the preceding
context is not well studied. In this paper, we propose a conversation-level RAG
approach, which incorporates fine-grained retrieval augmentation and self-check
for conversational question answering (CQA). In particular, our approach
consists of three components, namely conversational question refiner,
fine-grained retriever and self-check based response generator, which work
collaboratively for question understanding and relevant information acquisition
in conversational settings. Extensive experiments demonstrate the great
advantages of our approach over the state-of-the-art baselines. Moreover, we
also release a Chinese CQA dataset with new features including reformulated
question, extracted keyword, retrieved paragraphs and their helpfulness, which
facilitates further researches in RAG enhanced CQA.
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