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Cut Costs, Not Accuracy: LLM-Powered Data Processing with Guarantees

Created by
  • Haebom

Author

Sepanta Zeighami, Shreya Shankar, Aditya Parameswaran

Outline

This paper presents a cost-effective strategy for processing large-scale text data using large-scale language models (LLMs). We propose a novel method, BARGAIN, by improving the model cascade framework, which combines high-performance, expensive LLMs with low-cost, low-performance LLMs. BARGAIN efficiently utilizes low-cost LLMs through adaptive sampling strategies and statistical estimation procedures, reducing costs by up to 86% while providing strong theoretical guarantees on accuracy, precision, and recall. We verify these efficiency and guarantees through experiments on eight real-world datasets.

Takeaways, Limitations

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Takeaways:
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We present an efficient data processing strategy that dramatically reduces the cost of using expensive LLM while maintaining high accuracy.
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Provides strong theoretical guarantees for the target metrics among accuracy, precision, and recall.
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Experimentally verified to work effectively in real data processing environments through adaptive sampling strategies and statistical estimation procedures.
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Limitations:
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BARGAIN's performance may vary depending on the characteristics of the LLM used and the dataset. (Further research on generalizability is needed.)
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Since theoretical guarantees are established under certain assumptions, the validity of the assumptions must be reviewed when applying them in practice.
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Additional experimental results on other types of datasets beyond the eight presented real-world datasets are needed.
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