Prompt Engineer ：FrugalGPT to Minimize API Costs| GPT-4 API is Expensive
Reduce the cost of Using LLMs using Frugal-GPT.
Paper Link: https://arxiv.org/pdf/2305.05176.pdf
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In this video, we would review a paper titled “FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance”
In this paper, we outline and discuss practical strategies for reducing the inference cost of using LLM APIs. We also developed FrugalGPT to illustrate one of the cost-saving strategies, LLM cascade. Our empirical findings show that FrugalGPT can reduce costs by up to 98% while preserving the performance of cutting-edge LLMs.
There is a rapidly growing number of large language models (LLMs) that users can query for a fee. We review the cost associated with querying popular LLM APIs|e.g. GPT-4, ChatGPT, J1-Jumbo|and find that these models have heterogeneous pricing structures, with fees that can differ by two orders of magnitude. In particular, using LLMs on large collections of queries and text can be expensive. Motivated by this, we outline and discuss three types of strategies that users can exploit to reduce the inference cost associated with using LLMs: 1) prompt adaptation, 2) LLM approximation, and 3) LLM cascade. As an example, we propose FrugalGPT, a simple yet flexible instantiation of LLM cascade which learns which combinations of LLMs to use for different queries in order to reduce cost and improve accuracy. Our experiments show that FrugalGPT can match the performance of the best individual LLM (e.g. GPT-4) with up to 98% cost reduction or improve the accuracy over GPT-4 by 4% with the same cost. The ideas and findings presented here lay a foundation for using LLMs sustainably and efficiently.
Intro: 0:00
Start of Paper Review: 01:55
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