Summary of Gpt Semantic Cache: Reducing Llm Costs and Latency Via Semantic Embedding Caching, by Sajal Regmi et al.
GPT Semantic Cache: Reducing LLM Costs and Latency via Semantic Embedding Caching
by Sajal Regmi, Chetan Phakami Pun
First submitted to arxiv on: 8 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces GPT Semantic Cache, a method that leverages semantic caching to efficiently identify semantically similar user queries and retrieve pre-generated responses from Large Language Models (LLMs) like GPT. The approach stores query embeddings in Redis, reducing API calls by up to 68.8% across various query categories, with cache hit rates ranging from 61.6% to 68.8%. The system achieves high accuracy, with positive hit rates exceeding 97%, making it a robust solution for optimizing LLM-powered applications like customer service chatbots. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GPT Semantic Cache is a new way to make computers talk like humans by storing special codes called embeddings in memory. This helps machines quickly find answers they’ve already figured out before, so they don’t have to ask the big computer (LLM) all the time. This makes it cheaper and faster for computers to respond to user questions. The technique works really well, correctly answering over 97% of the time! |
Keywords
» Artificial intelligence » Gpt
