首届精炼可靠检索增强生成研讨会，聚焦RAG系统改进路径
R^3AG: First Workshop on Refined and Reliable Retrieval Augmented Generation
- 召集学界产业界共探RAG基础原理与实践方法
- 旨在提升RAG在信息检索与生成中的可靠性与适用性
- 适合关注大模型知识增强与RAG优化的研究者
检索增强生成（RAG）作为提升大语言模型外部知识注入的关键技术，已广泛应用于各类基于LLM的应用中。然而随着其广泛应用，越来越多问题与局限被发现，亟需深入探索以改进现有RAG框架。本次研讨会旨在深入探讨如何实现精细化、可靠的RAG，推动下游AI任务的性能提升。为此，我们将在SIGIR-AP 2024举办首届R3AG研讨会，邀请参与者重新审视并构建精炼可靠RAG的基本原则与实施路径。研讨会为学术界与工业界研究者提供交流平台，围绕基础挑战、前沿研究及优化路径展开讨论与报告。最终目标是形成对提升RAG可靠性与适用性的更清晰认知，强化信息检索与语言生成的协同能力。
原文摘要 · Abstract (English)
Retrieval-augmented generation (RAG) has gained wide attention as the key component to improve generative models with external knowledge augmentation from information retrieval. It has shown great prominence in enhancing the functionality and performance of large language model (LLM)-based applications. However, with the comprehensive application of RAG, more and more problems and limitations have been identified, thus urgently requiring further fundamental exploration to improve current RAG frameworks. This workshop aims to explore in depth how to conduct refined and reliable RAG for downstream AI tasks. To this end, we propose to organize the first R3AG workshop at SIGIR-AP 2024 to call for participants to re-examine and formulate the basic principles and practical implementation of refined and reliable RAG. The workshop serves as a platform for both academia and industry researchers to conduct discussions, share insights, and foster research to build the next generation of RAG systems. Participants will engage in discussions and presentations focusing on fundamental challenges, cutting-edge research, and potential pathways to improve RAG. At the end of the workshop, we aim to have a clearer understanding of how to improve the reliability and applicability of RAG with more robust information retrieval and language generation.
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