From Exact Hits to Close Enough: Semantic Caching for LLM Embeddings
arXiv:2603.03301v1 Announce Type: cross Abstract: The rapid adoption of large language models (LLMs) has created demand for faster responses and lower costs. Semantic caching, reusing semantically similar requests via their embeddings, addresses this need but breaks classic cache assumptions and raises new challenges. In this paper, we explore offline policies for semantic caching, proving that implementing an optimal offline policy is NP-hard, and propose several polynomial-time heuristics. We also present online semantic aware cache policies that combine recency, frequency, and locality. Evaluations on diverse datasets show that while frequency based policies are strong baselines, our novel variant improves semantic accuracy. Our findings reveal effective strategies for current systems and highlight substantial headroom for future innovation. All code is open source.
arXiv:2603.03301v1 Announce Type: cross Abstract: The rapid adoption of large language models (LLMs) has created demand for faster responses and lower costs. Semantic caching, reusing semantically similar requests via their embeddings, addresses this need but breaks classic cache assumptions and raises new challenges. In this paper, we explore offline policies for semantic caching, proving that implementing an optimal offline policy is NP-hard, and propose several polynomial-time heuristics. We also present online semantic aware cache policies that combine recency, frequency, and locality. Evaluations on diverse datasets show that while frequency based policies are strong baselines, our novel variant improves semantic accuracy. Our findings reveal effective strategies for current systems and highlight substantial headroom for future innovation. All code is open source.
Executive Summary
This article proposes semantic caching for large language model (LLM) embeddings, aiming to address the demand for faster responses and lower costs. The authors explore offline policies for semantic caching, proving that implementing an optimal offline policy is NP-hard, and propose several polynomial-time heuristics. They also present online semantic-aware cache policies that combine recency, frequency, and locality. Evaluations on diverse datasets show that their novel variant improves semantic accuracy, highlighting effective strategies for current systems and substantial headroom for future innovation.
Key Points
- ▸ Semantic caching for LLM embeddings addresses the demand for faster responses and lower costs.
- ▸ Implementing an optimal offline policy for semantic caching is NP-hard.
- ▸ Several polynomial-time heuristics are proposed for offline semantic caching.
- ▸ Online semantic-aware cache policies combine recency, frequency, and locality.
- ▸ Evaluations show that the novel variant improves semantic accuracy.
Merits
Comprehensive Analysis
The authors provide a thorough analysis of semantic caching for LLM embeddings, including the challenges and opportunities it presents.
Effective Strategies
The proposed online semantic-aware cache policies and polynomial-time heuristics offer effective strategies for current systems.
Substantial Headroom for Innovation
The article highlights substantial headroom for future innovation in the field of semantic caching for LLM embeddings.
Demerits
NP-Hardness Limitation
The limitation of implementing an optimal offline policy being NP-hard may hinder the adoption of semantic caching in certain contexts.
Dataset Dependence
The effectiveness of the proposed policies may depend on the specific datasets used, which could limit their generalizability.
Lack of Real-World Evaluation
The article does not provide real-world evaluation of the proposed policies, which could be a limitation in understanding their practical applicability.
Expert Commentary
The article provides a comprehensive analysis of semantic caching for LLM embeddings, highlighting both the opportunities and challenges it presents. The proposed online semantic-aware cache policies and polynomial-time heuristics offer effective strategies for current systems, and the article's findings suggest substantial headroom for future innovation. However, the limitation of implementing an optimal offline policy being NP-hard may hinder the adoption of semantic caching in certain contexts. Additionally, the effectiveness of the proposed policies may depend on the specific datasets used, which could limit their generalizability. Overall, the article provides valuable insights into the field of semantic caching for LLM embeddings and highlights the need for further research to address the challenges it presents.
Recommendations
- ✓ Further research is needed to develop more efficient polynomial-time heuristics for offline semantic caching.
- ✓ Real-world evaluation of the proposed policies should be conducted to understand their practical applicability.
- ✓ The article's findings should be considered as a strategic priority for organizations relying on large language models, including the development of more efficient semantic caching policies and the exploration of new use cases.