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

• URL: http://arxiv.org/abs/2505.11271v1
• Date: Fri, 16 May 2025 14:04:31 GMT
• Title: Semantic Caching of Contextual Summaries for Efficient Question-Answering with Language Models
• Authors: Camille Couturier, Spyros Mastorakis, Haiying Shen, Saravan Rajmohan, Victor Rühle,
• Abstract summary: Large Language Models (LLMs) are increasingly deployed across edge and cloud platforms for real-time question-answering and retrieval-augmented generation.<n>This paper introduces a novel semantic caching approach for storing and reusing contextual summaries.<n>Our method reduces redundant computations by up to 50-60% while maintaining answer accuracy comparable to full document processing.
• Score: 11.012474205717178
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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