Semantic Cache

Semantic caching aims to improve the efficiency and reduce the cost of large language model (LLM) and other query-based systems by storing and reusing semantically similar queries and their responses. Current research focuses on developing sophisticated algorithms, such as those incorporating contextual linguistic analysis and multi-head attention mechanisms, to accurately identify semantically equivalent queries and optimize cache storage and retrieval strategies. This work is significant because it addresses the high computational cost of LLMs, improving response times, reducing operational expenses, and mitigating privacy concerns associated with repeated queries, ultimately enhancing the user experience and scalability of various applications.