Summary of Superiority Of Multi-head Attention in In-context Linear Regression, by Yingqian Cui et al.
Superiority of Multi-Head Attention in In-Context Linear Regression
by Yingqian Cui, Jie Ren, Pengfei He, Jiliang Tang, Yue Xing
First submitted to arxiv on: 30 Jan 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 The researchers conduct a theoretical analysis to compare the performance of transformers with softmax attention in linear regression tasks. They find that multi-head attention with a large embedding dimension performs better than single-head attention, with a prediction loss that decreases as the number of examples increases. The study also considers various scenarios, including noisy labels and correlated features, and finds that multi-head attention is generally preferred. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, scientists compare how well transformers work for different types of learning. They looked at how good single-head attention (where there’s one “head” or way of paying attention) and multi-head attention (where there are many heads) are. They found that when there are lots of examples, the multi-head attention does better. This matters because it helps us understand how transformers can be used to make predictions. |
Keywords
* Artificial intelligence * Attention * Embedding * Linear regression * Multi head attention * Softmax
