Large Language Models (LLMs) showcase remarkable abilities, yet they struggle with limitations such as hallucinations, outdated knowledge, opacity, and inexplicable reasoning. To address these challenges, Retrieval-Augmented Generation (RAG) has proven to be a viable solution, leveraging external databases to improve the consistency and coherence of generated content, especially valuable for complex, knowledge-rich tasks, and facilitates continuous improvement by leveraging domain-specific insights. By combining the intrinsic knowledge of LLMs with the vast, dynamic repositories of external databases, RAG achieves a synergistic effect. However, RAG is not without its limitations, including a limited context window, irrelevant information, and the high processing overhead for extensive contextual data. In this comprehensive work, we explore the evolution of Contextual Compression paradigms, providing an in-depth examination of the field. Finally, we outline the current challenges and suggest potential research and development directions, paving the way for future advancements in this area.

本研究主要解决大型语言模型（LLMs）在生成内容时面临的幻觉、知识陈旧和推理不清等问题。通过检索增强生成（RAG）技术，结合LLMs的内在知识与外部数据库，本文提出了一种新的上下文压缩范式，并分析其演变和当前挑战，为未来的研究方向指明了道路。

检索增强生成中的上下文压缩：大型语言模型的综述