A + B：一个用于优化大语言模型以释放协同潜力的一般性生成器-读取器框架
A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential
AI总结：
本文提出通用“A + B”生成器-读取器框架，通过组合不同LLM版本并引入持续学习，系统提升复杂场景下的知识获取与推理性能，并解决外部知识集成后的安全性与有用性问题。
摘要：
检索增强生成（RAG）是向大语言模型（LLMs）补充必要知识的有效解决方案。针对其检索器性能瓶颈，提出了“先生成后读取”流水线，用LLM自身的生成替代检索阶段。尽管前景可观，但这一研究方向尚未得到充分探索，且在给定源知识的场景下仍无法工作。本文中，我们形式化了一个通用的“A + B”框架，通过基础模型和类型的不同组合进行系统性研究。我们探索了LLMs基础版和对话版的效能，发现它们的不同功能分别适合作为生成器A和读取器B。它们的组合始终优于单一模型，尤其是在复杂场景中。此外，我们通过持续学习将“A + B”框架的应用扩展到涉及源文档的场景，使外部知识能够直接集成到LLMs中。该方法不仅有助于有效获取新知识，还解决了适应后的安全性和有用性挑战。本文强调了“A + B”框架的多功能性，展示了其在各个领域增强LLMs实际应用的潜力。
英文摘要：
Retrieval-Augmented Generation (RAG) is an effective solution to supplement necessary knowledge to large language models (LLMs). Targeting its bottleneck of retriever performance, "generate-then-read" pipeline is proposed to replace the retrieval stage with generation from the LLM itself. Although promising, this research direction is underexplored and still cannot work in the scenario when source knowledge is given. In this paper, we formalize a general "A + B" framework with varying combinations of foundation models and types for systematic investigation. We explore the efficacy of the base and chat versions of LLMs and found their different functionalities suitable for generator A and reader B, respectively. Their combinations consistently outperform single models, especially in complex scenarios. Furthermore, we extend the application of the "A + B" framework to scenarios involving source documents through continuous learning, enabling the direct integration of external knowledge into LLMs. This approach not only facilitates effective acquisition of new knowledge but also addresses the challenges of safety and helpfulness post-adaptation. The paper underscores the versatility of the "A + B" framework, demonstrating its potential to enhance the practical application of LLMs across various domains.
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。
