Summary of Gpt-4o As the Gold Standard: a Scalable and General Purpose Approach to Filter Language Model Pretraining Data, by Jifan Zhang et al.
GPT-4o as the Gold Standard: A Scalable and General Purpose Approach to Filter Language Model Pretraining Data
by Jifan Zhang, Ziyue Luo, Jia Liu, Ness Shroff, Robert Nowak
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed paper presents a solution to filter high-quality training data from vast amounts of web-scale datasets. The GPT-4o model is demonstrated to be effective, but its prohibitive cost makes it impractical at this scale. To address this challenge, the authors propose SIEVE, a lightweight alternative that matches GPT-4o’s accuracy at less than 1% of the cost. SIEVE integrates GPT-4o and lightweight text classification models using active learning to fine-tune these models in the background with a small number of calls to GPT-4o. This approach enables efficient curation of high-quality data for general or specialized domains from web-scale corpora. The paper also presents extensive experiments using automatic and human evaluation metrics, demonstrating SIEVE’s performance on five specific filtering prompts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a big problem in making language models work better with less effort and money. Right now, it takes a lot of high-quality training data to make these models smart, but finding that data is like searching for a needle in a haystack. The researchers propose a new way called SIEVE that’s super good at picking out the best data from huge collections. It’s much cheaper than the old method and works almost as well! They tested it on different types of data and showed it can be used to make language models better. |
Keywords
» Artificial intelligence » Active learning » Gpt » Text classification
