ERAGent：提升检索增强语言模型的准确性、效率与个性化
ERAGent: Enhancing Retrieval-Augmented Language Models with Improved Accuracy, Efficiency, and Personalization
AI总结：
ERAGent通过协同模块提升检索质量、引入检索触发器减少冗余检索，并利用经验学习模块实现个性化，在六个数据集和三个问答任务上验证了其优越的准确性、效率与个性化。
摘要：
检索增强生成（RAG）显著提升了语言理解系统的性能。由于多种组件的集成，基本的“先检索后阅读”的响应生成流程已演变为更复杂的过程，有时甚至形成循环结构。尽管RAG在提高响应准确性方面取得了进展，但仍面临诸多挑战，例如：对于需要搜索多方面语义信息的复杂问题，检索质量不佳；在长期服务过程中，知识重复检索导致效率低下；以及缺乏个性化响应。为克服这些局限，我们引入了ERAGent，这是一个代表RAG领域先进水平的前沿框架。我们的贡献在于引入了协同运作的模块：增强型问题重写器和知识过滤器，以提升检索质量。同时，我们融入了检索触发器，以减少不必要的外部知识检索，同时不牺牲响应质量。ERAGent还通过整合学习到的用户画像来实现个性化响应。ERAGent的效率和个性化特性由经验学习模块支撑，该模块使AI助手能够逐步扩展其知识并建模用户画像。在六个数据集和三个问答任务上的严格评估证明，ERAGent在准确性、效率和个性化方面表现卓越，凸显了其推动RAG领域发展的潜力及其在实际系统中的适用性。
英文摘要：
Retrieval-augmented generation (RAG) for language models significantly improves language understanding systems. The basic retrieval-then-read pipeline of response generation has evolved into a more extended process due to the integration of various components, sometimes even forming loop structures. Despite its advancements in improving response accuracy, challenges like poor retrieval quality for complex questions that require the search of multifaceted semantic information, inefficiencies in knowledge re-retrieval during long-term serving, and lack of personalized responses persist. Motivated by transcending these limitations, we introduce ERAGent, a cutting-edge framework that embodies an advancement in the RAG area. Our contribution is the introduction of the synergistically operated module: Enhanced Question Rewriter and Knowledge Filter, for better retrieval quality. Retrieval Trigger is incorporated to curtail extraneous external knowledge retrieval without sacrificing response quality. ERAGent also personalizes responses by incorporating a learned user profile. The efficiency and personalization characteristics of ERAGent are supported by the Experiential Learner module which makes the AI assistant being capable of expanding its knowledge and modeling user profile incrementally. Rigorous evaluations across six datasets and three question-answering tasks prove ERAGent's superior accuracy, efficiency, and personalization, emphasizing its potential to advance the RAG field and its applicability in practical systems.
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。
