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FrugalGPT is a research framework that helps reduce the cost of using large language models (LLMs) while improving performance. It outlines strategies like prompt adaptation, LLM approximation, and LLM cascade to achieve significant cost reductions.
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FrugalGPT is a novel research framework designed to optimize the usage of large language models (LLMs) by significantly reducing associated costs and enhancing performance. It addresses the heterogeneous and often expensive pricing structures of popular LLM APIs such as GPT-4, ChatGPT, and J1-Jumbo. The framework proposes three core strategies: prompt adaptation, LLM approximation, and LLM cascade. As a practical example, FrugalGPT introduces an LLM cascade system that intelligently learns to combine different LLMs for various queries, aiming to minimize cost while maximizing accuracy. Experiments demonstrate that FrugalGPT can achieve up to a 98% cost reduction while matching the performance of leading individual LLMs like GPT-4, or improve accuracy by 4% at the same cost. This research provides a foundation for sustainable and efficient LLM utilization.
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Not publicly disclosed. Check arxiv.org for current pricing.
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