Summary of The Responsible Foundation Model Development Cheatsheet: a Review Of Tools & Resources, by Shayne Longpre et al.
The Responsible Foundation Model Development Cheatsheet: A Review of Tools & Resources
by Shayne Longpre, Stella Biderman, Alon Albalak, Hailey Schoelkopf, Daniel McDuff, Sayash Kapoor, Kevin Klyman, Kyle Lo, Gabriel Ilharco, Nay San, Maribeth Rauh, Aviya Skowron, Bertie Vidgen, Laura Weidinger, Arvind Narayanan, Victor Sanh, David Adelani, Percy Liang, Rishi Bommasani, Peter Henderson, Sasha Luccioni, Yacine Jernite, Luca Soldaini
First submitted to arxiv on: 24 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 paper introduces the Foundation Model Development Cheatsheet, a comprehensive collection of 250+ tools and resources for responsible development practices in foundation model development. The cheatsheet covers text, vision, and speech modalities, providing resources for informed data selection, processing, and understanding, as well as precise artifact documentation, efficient model training, and careful evaluation of capabilities, risks, and claims. The paper highlights the importance of responsible model release, licensing, and deployment practices. A review of the AI development ecosystem reveals critical gaps in tools for data sourcing, model evaluation, and monitoring, as well as a lack of transparency and reproducibility in evaluations for model safety, environmental impact, and capabilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about creating a helpful guide for people who develop artificial intelligence models. The guide, called the Foundation Model Development Cheatsheet, has many tools and resources to help developers make responsible choices when building AI models. It covers things like choosing data, training models, and evaluating their capabilities. The paper also looks at the current state of AI development and finds some big gaps in what’s available to help developers make good decisions. Overall, the goal is to create a better way to develop AI that benefits everyone. |
