Emergent Mind

FrugalGPT: How to Use Large Language Models While Reducing Cost and Improving Performance

(2305.05176)

Published May 9, 2023 in cs.LG , cs.AI , cs.CL , and cs.SE

Abstract

There is a rapidly growing number of 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.

FrugalGPT achieves similar performance to GPT-4 with significantly lower costs, potentially improving accuracy by 5%.

Overview

FrugalGPT introduces strategies to reduce costs and maintain or improve performance in the deployment of LLMs such as GPT-4.
It offers methods like prompt adaptation, LLM approximation, and LLM cascade to achieve cost savings and accuracy.
The solution provides up to 98% cost reduction or a 4% increase in accuracy at equal expenditure compared to traditional LLM use.
FrugalGPT emphasizes the importance of balancing cost, performance, and societal impacts in the ongoing development and application of LLMs.

FrugalGPT: Enhancing Cost-Effectiveness and Performance in Large Language Model Utilization

Introduction

The proliferation of LLMs has brought about a transformative shift across various domains, offering unparalleled capabilities in natural language understanding and generation. However, the deployment of these models, especially for high-volume applications, is tethered to significant financial and environmental costs. In response to these challenges, this paper introduces FrugalGPT, a solution designed to navigate the cost-performance trade-offs inherent in the utilization of LLMs. Through an innovative application of prompt adaptation, LLM approximation, and LLM cascade strategies, FrugalGPT demonstrates the ability to either match the performance of leading models such as GPT-4 with up to 98% cost reduction or achieve superior accuracy at the equivalent expenditure.

Strategies for Cost-Effective LLM Use

FrugalGPT embodies a flexible framework that incorporates three pivotal strategies for reducing LLM inference costs without compromising on performance:

Prompt Adaptation: This entails optimizing prompt length and content to minimize query costs. Techniques such as prompt selection and query concatenation are deployed to reduce prompt size, thereby lowering associated costs.
LLM Approximation: This involves substituting expensive LLM queries with more cost-effective alternatives without significant loss in accuracy. Strategies like completion cache and model fine-tuning fall under this category, leveraging cached responses and fine-tuned smaller models to deliver cost efficiencies.
LLM Cascade: At the core of FrugalGPT, LLM cascade strategically sequences the use of different LLMs based on the complexity of queries. It intelligently determines which LLM to query based on a cost-accuracy trade-off, potentially starting with less expensive models and escalating to more costly options only as needed.

Empirical Validation

The effectiveness of FrugalGPT was rigorously evaluated across several datasets, demonstrating substantial cost savings along with performance retention or enhancement. Experiments highlighted the capacity of FrugalGPT to:

Achieve up to 98% cost reduction while maintaining the accuracy levels of premier LLMs like GPT-4.
Improve accuracy by 4% at the same expenditure, compared to using a single LLM such as GPT-4.
Present a compelling case for the financial and environmental sustainability of LLM deployment, particularly for SMEs and high-throughput applications.

Discussion and Future Directions

FrugalGPT presents a viable pathway toward enhancing the cost-effectiveness and sustainability of utilizing LLMs. Its approach, centered around strategic cascading and intelligent resource allocation, sets a precedent for future developments in managing LLM inference costs. However, it is essential to recognize the necessity of labeled data for training the cascade strategy and the upfront resources required for implementation. As LLM technology continues to evolve, expanding on the foundations laid by FrugalGPT to include considerations around latency, fairness, privacy, and environmental impact will be crucial. The quest to balance performance, cost, and broader societal implications remains a dynamic and evolving challenge, urging continuous research and innovation in the field.

Conclusion

FrugalGPT offers a pioneering solution to the significant challenge of leveraging LLMs in a cost-effective manner without sacrificing performance. By employing strategies such as prompt adaptation, LLM approximation, and an LLM cascade framework, FrugalGPT paves the way for the broader, more sustainable application of these powerful models across industries. As the landscape of LLMs progresses, there is a clear imperative to refine and expand upon these strategies, ensuring that the deployment of LLMs remains financially viable and environmentally responsible. The journey of FrugalGPT marks a significant step forward in this ongoing endeavor, providing valuable insights and methodologies for the future of LLM utilization.