Summary of Mlps Learn In-context on Regression and Classification Tasks, by William L. Tong and Cengiz Pehlevan
MLPs Learn In-Context on Regression and Classification Tasks
by William L. Tong, Cengiz Pehlevan
First submitted to arxiv on: 24 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 challenges the notion that Transformer models are uniquely capable of in-context learning (ICL), a task where a model solves a problem based solely on input examples. By examining synthetic ICL tasks, researchers found that multi-layer perceptrons (MLPs) can also learn in-context. Interestingly, MLPs and MLP-Mixer models learned comparably to Transformers within the same compute budget. The study also shows that MLPs outperformed Transformers on classical psychology tasks designed to test relational reasoning. These findings highlight the importance of exploring ICL beyond attention-based architectures and challenge prior assumptions about MLPs’ capabilities. The paper encourages further investigation into these architectures in more complex settings to understand their comparative advantages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well different types of artificial intelligence models do when they only get input examples to solve a problem. Most people think that special AI models called Transformers are really good at this, but the researchers found that other models, like multi-layer perceptrons (MLPs), can also do it just as well. The study shows that MLPs and similar models are actually better than Transformers on some types of problems. This is important because it means we should think about using different types of AI models for certain tasks, rather than always relying on the same ones. |
Keywords
» Artificial intelligence » Attention » Transformer
