Summary of A Theoretical Survey on Foundation Models, by Shi Fu et al.
A Theoretical Survey on Foundation Models
by Shi Fu, Yuzhu Chen, Yingjie Wang, Dacheng Tao
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 A new class of interpretable methods is needed to understand the inner mechanisms of black-box foundation models (FMs) in artificial intelligence applications. While post-hoc explainable methods have been developed, they have limitations in terms of faithfulness and resource requirements. This survey reviews interpretable methods that are accurate, comprehensive, heuristic, and resource-light, and have been successfully applied to FMs. The methods analyzed include machine learning theory-based approaches that provide a thorough interpretation of the entire workflow of FMs, covering inference capability, training dynamics, and ethical implications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Black-box foundation models (FMs) are used in artificial intelligence applications, but understanding how they work is challenging. Researchers have developed ways to explain why FMs make certain decisions, but these methods have limits. Instead, a new approach is needed that can accurately show how FMs think and learn. This review looks at different methods that do this, and finds that they provide a clear understanding of how FMs work, from training to making predictions. |
Keywords
» Artificial intelligence » Inference » Machine learning
