Summary of The Alchemist: Automated Labeling 500x Cheaper Than Llm Data Annotators, by Tzu-heng Huang et al.
The ALCHEmist: Automated Labeling 500x CHEaper Than LLM Data Annotators
by Tzu-Heng Huang, Catherine Cao, Vaishnavi Bhargava, Frederic Sala
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 In this paper, researchers introduce Alchemist, a system that utilizes large pretrained models as annotators to generate programs that can produce labels, instead of directly querying labels from these models. This approach enables the creation of smaller specialist models at a fraction of the cost of traditional methods. The proposed system obtains comparable or better performance in various tasks while reducing labeling costs by approximately 500 times. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Alchemist is a new way to use big language models to help us make datasets. Normally, we pay a lot of money to ask these models questions and get answers back. But this can be expensive and hard to control. Alchemist changes things by asking the models to create small programs that can give us the answers we need. These programs are cheap to run, easy to use, and work well in many situations. |
