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arXiv · 2505.11271

Semantic Caching of Contextual Summaries for Efficient Question-Answering with Language Models

Camille Couturier·Spyros Mastorakis·Haiying Shen·Saravan Rajmohan·Victor Rühle
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Abstract

Large Language Models (LLMs) are increasingly deployed across edge and cloud platforms for real-time question-answering and retrieval-augmented generation. However, processing lengthy contexts in distributed systems incurs high computational overhead, memory usage, and network bandwidth. This paper introduces a novel semantic caching approach for storing and reusing intermediate contextual summaries, enabling efficient information reuse across similar queries in LLM-based QA workflows. Our method reduces redundant computations by up to 50-60% while maintaining answer accuracy comparable to full document processing, as demonstrated on NaturalQuestions, TriviaQA, and a synthetic ArXiv dataset. This approach balances computational cost and response quality, critical for real-time AI assistants.

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Camille Couturier, Spyros Mastorakis, Haiying Shen, Saravan Rajmohan, Victor Rühle. 2025-05-16. Semantic Caching of Contextual Summaries for Efficient Question-Answering with Language Models. https://arxiv.org/abs/2505.11271

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