Contextual Compression in Retrieval-Augmented Generation for Large Language Models: A Survey
<|reference_start|>Contextual Compression in Retrieval-Augmented Generation for Large Language Models: A Survey: Large Language Models (LLMs) showcase remarkable abilities, yet they struggle with limitations such as hallucinations, outdated knowledge, opacity, and inexplicable reasoning.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.<|reference_end|>
@article{verma2024contextual,
    title={Contextual Compression in Retrieval-Augmented Generation for Large
  Language Models: A Survey},
    author={Sourav Verma},
    journal={arXiv preprint arXiv:2409.13385},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.13385},
    primaryClass={cs.CL cs.IR}
}