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Message definition (FACTS About Building Retrieval Augmented Generation-based Chatbots ) Active Retrieval augmented generation (FLARE) ([[#bib.bib7|7]]) iteratively synthesizes a hypothetical next sentence. If the generated sentence contains low-probability tokens, FLARE would use the sentence as the new query for retrieval and regenerate the sentence. Mialon ''et al.'' ([[#bib.bib12|12]]) reviews works for advanced augmented generation methods in language model. Self-refine ([[#bib.bib11|11]]) builds an agent to improve the initial answer of RAG through iterative feedback and refinement. ReAct ([[#bib.bib16|16]]) Agent is widely used for handling the complex queries in a recursive manner. On the RAG evaluation front, RAGAS ([[#bib.bib4|4]]) and ARES ([[#bib.bib14|14]]) utilize LLMs as judges and build automatic RAG benchmark to evaluate the RAG system. Zhu ''et al.'' ([[#bib.bib17|17]]) overview the intensive usages of LLM in a RAG pipeline including retriever, data generation, rewriter, and reader. We believe that our work provides a unique perspective on building secure enterprise-grade chatbots via our FACTS framework.
主动检索增强生成（FLARE）（7 ）迭代合成假设的下一句。如果生成的句子包含低概率的词汇，FLARE将使用该句子作为新的检索查询并重新生成句子。Mialon et al. （12 ）回顾了语言模型中高级增强生成方法的相关工作。Self-refine（11 ）构建了一个代理，通过迭代反馈和改进来提升RAG的初始答案。ReAct（16 ）代理被广泛用于以递归方式处理复杂查询。在RAG评估方面，RAGAS（4 ）和ARES（14 ）利用LLM作为评判者，并建立自动RAG基准来评估RAG系统。Zhu et al. （17 ）概述了LLM在RAG流程中的密集使用，包括检索器、数据生成、重写器和阅读器。我们相信，通过我们的FACTS框架，我们的工作为构建安全的企业级聊天机器人提供了独特的视角。
