Enhancing RAG Efficiency with Adaptive Context Compression

2025-07-24 · Shuyu Guo, Shuo Zhang, Zhaochun Ren arxiv

Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge but incurs significant inference costs due to lengthy retrieved contexts. While context compression mitigates this issue, existing methods apply fixed compression rates, over-compressing simple queries or under-compressing complex ones. We propose Adaptive Context Compression for RAG (ACC-RAG), a framework that dynamically adjusts compression rates based on input complexity, optimizing inference efficiency without sacrificing accuracy. ACC-RAG combines a hierarchical compressor (for multi-granular embeddings) with a context selector to retain minimal sufficient information, akin to human skimming. Evaluated on Wikipedia and five QA datasets, ACC-RAG outperforms fixed-rate methods and matches/unlocks over 4 times faster inference versus standard RAG while maintaining or improving accuracy.

📄 PDF Abstract BibTeX arXiv:2507.22931

Code (0)

등록된 구현이 없습니다.

EXIT: Context-Aware Extractive Compression for Enhancing Retrieval-Augmented Generation

2024-12-17 · Taeho Hwang, Sukmin Cho, Soyeong Jeong, Hoyun Song 외

We introduce EXIT, an extractive context compression framework that enhances both the effectiveness and efficiency of retrieval-augmented generation (RAG) in question answering (QA). Current RAG systems often struggle wh…

Question Answering RAG Retrieval Retrieval-augmented Generation+1

Enhancing Cache-Augmented Generation (CAG) with Adaptive Contextual Compression for Scalable Knowledge Integration

2025-05-13 · Rishabh Agrawal, Himanshu Kumar

The rapid progress in large language models (LLMs) has paved the way for novel approaches in knowledge-intensive tasks. Among these, Cache-Augmented Generation (CAG) has emerged as a promising alternative to Retrieval-Au…

RAG Retrieval Retrieval-augmented Generation World Knowledge

ATACompressor: Adaptive Task-Aware Compression for Efficient Long-Context Processing in LLMs

2026-02-03 · Xuancheng Li, Haitao Li, Yujia Zhou, Qingyao Ai 외 arxiv

Long-context inputs in large language models (LLMs) often suffer from the "lost in the middle" problem, where critical information becomes diluted or ignored due to excessive length. Context compression methods aim to ad…

Developing Adaptive Context Compression Techniques for Large Language Models (LLMs) in Long-Running Interactions

2026-03-31 · Payal Fofadiya, Sunil Tiwari arxiv

Large Language Models (LLMs) often experience performance degradation during long-running interactions due to increasing context length, memory saturation, and computational overhead. This paper presents an adaptive cont…

Computational Efficiency

ZipRL: Adaptive Multi-Turn Context Compression with Hindsight Response Replay

2026-05-27 · Zhexin Hu, Li Wang, Xiaohan Wang, Jiajun Chai 외 arxiv

Adaptive context compression is vital for scaling Large Language Models (LLMs) to complex, multi-turn agent tasks. However, rule-based compression methods may discard task-critical nuances, while Reinforcement Learning (…

Reinforcement Learning