Summary of Do Efficient Transformers Really Save Computation?, by Kai Yang et al.
Do Efficient Transformers Really Save Computation?
by Kai Yang, Jan Ackermann, Zhenyu He, Guhao Feng, Bohang Zhang, Yunzhen Feng, Qiwei Ye, Di He, Liwei Wang
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); 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 paper aims to explore the capabilities and limitations of efficient transformers, specifically the Sparse Transformer and Linear Transformer, in solving dynamic programming (DP) problems. While these models have been proposed as alternatives to the standard Transformer, they lack theoretical guarantees of being suitable replacements. The authors focus on the reasoning capability of these models using Chain-of-Thought prompts, modeling them as DP problems. The results show that while these models are expressive enough to solve general DP tasks, they require a model size that scales with the problem size. However, the authors identify a class of DP problems where these models can be more efficient than the standard Transformer. Experiments on representative DP tasks confirm the theoretical results, providing insights into the practical strengths and weaknesses of efficient transformers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at new ways to make language models faster without sacrificing their ability to understand and respond to questions. Right now, these models are getting bigger and bigger, but they’re not always using that extra power effectively. The authors investigate two special kinds of transformers that might be more efficient than the regular kind. They test how well these new transformers can solve certain types of problems and find that while they’re good at solving some problems, they need to get bigger and more complicated just like the original model. However, the authors do identify a type of problem where their new transformers perform better than the old one. This helps us understand what makes these new models tick and how we can use them best. |
Keywords
* Artificial intelligence * Transformer
