Summary of Automated Statistical Model Discovery with Language Models, by Michael Y. Li et al.
Automated Statistical Model Discovery with Language Models
by Michael Y. Li, Emily B. Fox, Noah D. Goodman
First submitted to arxiv on: 27 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 method for language model driven automated statistical model discovery leverages large language models (LMs) to efficiently search over a vast space of statistical models subject to domain-specific constraints. By casting the procedure within Box’s Loop, LMs act as both modeler and domain expert, proposing probabilistic programs representing statistical models and critiquing them based on domain knowledge. This approach eliminates the need for defining a domain-specific language of models or designing a handcrafted search procedure, which are key restrictions of previous systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research proposes a new way to find good statistical models using big language models like those used in chatbots and language translation. It’s like having a super smart friend who can help you come up with different ideas for how to model something, then figure out if they make sense or not. This method is useful because it means we don’t have to tell the computer exactly what kind of model to use or how to search for them, which makes it more powerful and flexible. |
Keywords
* Artificial intelligence * Language model * Statistical model * Translation
