Summary of Circuit Complexity Bounds For Rope-based Transformer Architecture, by Bo Chen et al.
Circuit Complexity Bounds for RoPE-based Transformer Architecture
by Bo Chen, Xiaoyu Li, Yingyu Liang, Jiangxuan Long, Zhenmei Shi, Zhao Song
First submitted to arxiv on: 12 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computational Complexity (cs.CC); 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 explores the limitations and scaling laws of the Transformer architecture, particularly in regards to its express power and potential for large language models. Recent works have established circuit complexity bounds for Transformer-like architectures, but this study focuses on the Rotary Position Embedding (RoPE) technique, which has shown superior performance in capturing positional information. The authors establish a circuit complexity bound for Transformers with RoPE attention, demonstrating that certain problems cannot be solved by these models, despite their empirical success. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how well the Transformer architecture can handle different tasks and what limits its ability to do so. It focuses on a specific part of this called Rotary Position Embedding (RoPE), which helps large language models work better. The authors show that there are certain problems RoPE-based Transformers just can’t solve, even though they’re really good at some things. |
Keywords
» Artificial intelligence » Attention » Embedding » Scaling laws » Transformer
