SIEVE: General Purpose Data Filtering System Matching GPT-4o Accuracy at 1% the Cost

• URL: http://arxiv.org/abs/2410.02755v2
• Date: Tue, 8 Oct 2024 18:30:39 GMT
• Title: SIEVE: General Purpose Data Filtering System Matching GPT-4o Accuracy at 1% the Cost
• Authors: Jifan Zhang, Robert Nowak,
• Abstract summary: SIEVE is a lightweight filter that matches GPT-4o accuracy at a fraction of the cost. We experimentally validate SIEVE on the OpenWebText dataset, using five highly customized filter tasks. Our results demonstrate the effectiveness and efficiency of our method in curating large, high-quality datasets for language model training.
• Score: 8.406910685074134
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Creating specialized large language models requires vast amounts of clean, special purpose data for training and fine-tuning. With only a handful of existing large-scale, domain-specific datasets, creation of new datasets is required in most applications. This requires the development of new application-specific filtering of web-scale data. Filtering with a high-performance, general-purpose LLM such as GPT-4o can be highly effective, but this is extremely expensive at web-scale. This paper proposes SIEVE, a lightweight alternative that matches GPT-4o accuracy at a fraction of the cost. SIEVE can perform up to 500 filtering operations for the cost of one GPT-4o filtering call. The key to SIEVE is a seamless integration of GPT-4o and lightweight T5 models, using active learning to fine-tune T5 in the background with a small number of calls to GPT-4o. Once trained, it performs as well as GPT-4o at a tiny fraction of the cost. We experimentally validate SIEVE on the OpenWebText dataset, using five highly customized filter tasks targeting high quality and domain-specific content. Our results demonstrate the effectiveness and efficiency of our method in curating large, high-quality datasets for language model training at a substantially lower cost (1%) than existing techniques. To further validate SIEVE, experiments show that SIEVE and GPT-4o achieve similar accuracy, with human evaluators preferring SIEVE's filtering results to those of GPT-4o.

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