Boosting Conversational Question Answering with Fine-Grained Retrieval-Augmentation and Self-Check

Retrieval-Augmented Generation (RAG) aims to generate more reliable Conversational Question Answering (CQA) has attracted great and accurate responses, by augmenting large language models attention in both academia and industry in recent years, which (LLMs) with the external vast and dynamic knowledge. Most previous provides more natural human-computer interactions by extending work focuses on using RAG for single-round question answering, single-turn question answering (QA) to conversational settings [23, while how to adapt RAG to the complex conversational setting 33]. In CQA, users usually ask multiple follow-up questions using wherein the question is interdependent on the preceding context is anaphora that refers to certain concepts in previous conversation not well studied. In this paper, we propose a conversation-level RAG history, or ellipsis that can be omitted. As shown in Figure 1, the (ConvRAG) approach, which incorporates fine-grained retrieval augmentation'battle' in the current question refers to'Hunayn' in the first turn, and self-check for conversational question answering making it more challenging than single-turn QA. (CQA). In particular, our approach consists of three components, One key challenge in CQA is how to explicitly represent the namely conversational question refiner, fine-grained retriever and questions based on the interdependent context. Previous work focuses self-check based response generator, which work collaboratively on using the question rewriting methods for a better question for question understanding and relevant information acquisition understanding. Elgoharyet et al. [11] first released a dataset with in conversational settings. Extensive experiments demonstrate the human rewrites of questions and analysed the writing quality.