Advancing Semantic Caching for LLMs with Domain-Specific Embeddings and Synthetic Data

2025-04-03

AI Summary

To address the longstanding trade-off among accuracy, query latency, and computational efficiency in semantic caching, this paper proposes a domain-oriented lightweight embedding optimization framework. Methodologically: (1) we introduce the first synthetic data generation pipeline specifically designed for semantic caching; (2) we perform a single-phase fine-tuning of compact domain-specific embedding models, integrating domain adaptation and synthetic data augmentation; and (3) we enable low-overhead semantic similarity retrieval and efficient model deployment. Experiments demonstrate that our approach consistently outperforms state-of-the-art open-source and commercial embedding models in both precision and recall. Empirical evaluation shows over 40% reduction in query latency, substantial improvements in cache hit rate and system throughput, and seamless integration into LLM-driven semantic caching pipelines. The framework establishes a practical, high-performance paradigm for production-grade semantic caching.

Technology Category

• Search and Optimization: Learning to Search
• Natural Language Processing: Sentence-level Semantics, Textual Inference, etc.
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

This report investigates enhancing semantic caching effectiveness by employing specialized, fine-tuned embedding models. Semantic caching relies on embedding similarity rather than exact key matching, presenting unique challenges in balancing precision, query latency, and computational efficiency. We propose leveraging smaller, domain-specific embedding models, fine-tuned with targeted real-world and synthetically generated datasets. Our empirical evaluations demonstrate that compact embedding models fine-tuned for just one epoch on specialized datasets significantly surpass both state-of-the-art open-source and proprietary alternatives in precision and recall. Moreover, we introduce a novel synthetic data generation pipeline for the semantic cache that mitigates the challenge of limited domain-specific annotated data, further boosting embedding performance. Our approach effectively balances computational overhead and accuracy, establishing a viable and efficient strategy for practical semantic caching implementations.

Problem

• Enhancing semantic caching effectiveness with domain-specific embeddings
• Balancing precision, query latency, and computational efficiency in caching
• Overcoming limited annotated data via synthetic data generation

Innovation

• Domain-specific fine-tuned embedding models
• Synthetic data generation pipeline
• Balanced computational overhead and accuracy