Summary of Multi-layer Transformers Gradient Can Be Approximated in Almost Linear Time, by Yingyu Liang et al.
Multi-Layer Transformers Gradient Can be Approximated in Almost Linear Time
by Yingyu Liang, Zhizhou Sha, Zhenmei Shi, Zhao Song, Yufa Zhou
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 presents a novel approach to significantly reduce the computational complexity of self-attention mechanisms in transformer architectures, making it possible to efficiently train and deploy long-input language models. By developing a fast approximation method that calculates gradients in almost linear time (n^1+o(1)), while maintaining a polynomially small error (1/poly(n)) across the entire model, this work addresses the bottleneck of quadratic time complexity. This breakthrough has implications for the development and deployment of long-context language models, enabling more effective training and inference. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a major problem with transformer models that process long inputs. It’s like trying to do a big math problem really fast on your phone – it gets slow! The researchers found a new way to make the calculations faster, without sacrificing accuracy. This will help make language processing even better and more efficient. |
Keywords
» Artificial intelligence » Inference » Self attention » Transformer
