Large Language Models (LLMs) showcase remarkable abilities, yet they struggle with limitations such as hallucinations, outdated knowledge, opacity, and inexplicable reasoning.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.

翻译：大型语言模型（LLMs）展现出卓越的能力，但仍面临幻觉、知识过时、不透明及推理过程难以解释等局限。为应对这些挑战，检索增强生成（RAG）被证明是一种有效的解决方案，其通过利用外部数据库来提升生成内容的一致性与连贯性，对于复杂且知识密集的任务尤为宝贵，并能借助领域特定知识实现持续优化。通过将LLMs的内在知识与庞大动态的外部数据库相结合，RAG实现了协同增效。然而，RAG自身也存在局限，包括有限的上下文窗口、无关信息干扰以及处理海量上下文数据时的高昂开销。在本综述中，我们系统梳理了上下文压缩范式的发展脉络，对该领域进行了深入剖析。最后，我们总结了当前面临的挑战，并提出了潜在的研究与发展方向，以期为该领域的未来进展铺平道路。