SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression

• URL: http://arxiv.org/abs/2507.05633v1
• Date: Tue, 08 Jul 2025 03:29:09 GMT
• Title: SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression
• Authors: Yiqiao Jin, Kartik Sharma, Vineeth Rakesh, Yingtong Dou, Menghai Pan, Mahashweta Das, Srijan Kumar,
• Abstract summary: We propose SARA, a unified RAG framework that balances local precision and global knowledge coverage under tight context budgets.<n> SARA combines natural-language text snippets with semantic compression vectors to jointly enhance context efficiency and answer correctness.
• Score: 28.043964124611026
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Retrieval-augmented Generation (RAG) extends large language models (LLMs) with external knowledge but faces key challenges: restricted effective context length and redundancy in retrieved documents. Pure compression-based approaches reduce input size but often discard fine-grained details essential for factual accuracy. We propose SARA, a unified RAG framework that balances local precision and global knowledge coverage under tight context budgets. SARA combines natural-language text snippets with semantic compression vectors to jointly enhance context efficiency and answer correctness. It represents contexts at two complementary levels: 1) fine-grained natural-language spans that preserve critical entities and numerical values, and 2) compact, interpretable vectors that summarize high-level semantics. An iterative evidence-selection module employs the compression vectors for dynamic reranking of contexts. Across 9 datasets and 5 open-source LLMs spanning 3 model families (Mistral, Llama, and Gemma), SARA consistently improves answer relevance (+17.71), answer correctness (+13.72), and semantic similarity (+15.53), demonstrating the importance of integrating textual and compressed representations for robust, context-efficient RAG.

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