Summary of Little Giants: Synthesizing High-quality Embedding Data at Scale, by Haonan Chen et al.
Little Giants: Synthesizing High-Quality Embedding Data at Scale
by Haonan Chen, Liang Wang, Nan Yang, Yutao Zhu, Ziliang Zhao, Furu Wei, Zhicheng Dou
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR)
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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 This paper introduces SPEED, a framework that uses open-source small models to generate large-scale synthetic embedding data, efficiently reducing the need for human annotation. By aligning these models through fine-tuning, preference optimization, and self-improvement, SPEED produces high-quality data, outperforming state-of-the-art models like E5_mistral when trained solely on synthetic data. This efficient generator is remarkable in that it uses less than 1/10 of the GPT API calls, demonstrating its scalability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes a big breakthrough in artificial intelligence! Scientists used to need huge amounts of labeled data to train machines to understand things like text. But now, they can create fake data that’s just as good and it takes way less effort. This new system, called SPEED, uses small models to make this fake data. It’s really fast and works better than other methods. The scientists tested it and found out what makes the best fake data. |
Keywords
» Artificial intelligence » Embedding » Fine tuning » Gpt » Optimization » Synthetic data
