利用细粒度检索增强与自检查提升对话式问答
Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check
AI总结：
本文提出一种结合细粒度检索增强与自检查的对话级RAG方法，包含问题重写器、细粒度检索器和回复生成器三组件，有效解决复杂对话场景下的问答问题，并发布了一个中文CQA数据集。
摘要：
检索增强生成（RAG）旨在通过利用外部海量且动态的知识来增强大型语言模型（LLMs），从而生成更可靠和准确的回复。以往大多数工作侧重于将RAG用于单轮问答，而如何将RAG适应于问题与前文语境相互依赖的复杂对话场景，尚未得到充分研究。本文提出一种对话级RAG方法，该方法结合了细粒度检索增强和自检查，用于对话式问答（CQA）。具体而言，该方法包含三个组件：对话问题重写器、细粒度检索器和基于自检查的回复生成器，它们协同工作以在对话场景中实现问题理解和相关信息获取。大量实验表明，该方法相较于最先进的基线模型具有巨大优势。此外，我们还发布了一个中文CQA数据集，包含重写问题、提取关键词、检索段落及其有用性等新特征，以促进RAG增强CQA的进一步研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
