Summary of The Fine-grained Complexity Of Gradient Computation For Training Large Language Models, by Josh Alman et al.
The Fine-Grained Complexity of Gradient Computation for Training Large Language Models
by Josh Alman, Zhao Song
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computational Complexity (cs.CC); Computation and Language (cs.CL); Data Structures and Algorithms (cs.DS)
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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 computational complexity of large language model (LLM) training, focusing on the alternating forward and backward computations required to train these models. Building upon previous work by Alman and Song, this study demonstrates that while the forward computation can be performed in almost-linear time in certain regimes, there is no truly sub-quadratic algorithm for the remaining regimes unless a popular hypothesis called SETH is false. The researchers also show similar results for the harder-seeming problem of computing the gradient of loss function of one-layer attention networks, effectively characterizing the fine-grained complexity of every step in LLM training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to train large language models efficiently. Large language models are really good at understanding and generating human-like text, but they take a long time to learn. The researchers studied two important parts of this learning process: one that helps the model focus on certain words or phrases (called attention), and another that calculates how well the model did on a particular task. They found that these steps can be done quickly in some cases, but not always unless a certain rule called SETH is false. This research helps us understand how to make large language models learn faster. |
Keywords
* Artificial intelligence * Attention * Large language model * Loss function
