Summary of Can a Large Language Model Learn Matrix Functions in Context?, by Paimon Goulart and Evangelos E. Papalexakis
Can a Large Language Model Learn Matrix Functions In Context?
by Paimon Goulart, Evangelos E. Papalexakis
First submitted to arxiv on: 24 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper investigates the ability of Large Language Models (LLMs) to solve complex mathematical tasks through In-Context Learning (ICL). Specifically, it explores LLMs’ capacity to perform non-linear numerical computations, focusing on functions involving Singular Value Decomposition. The results show that while LLMs are comparable to traditional models like Linear Regression and Neural Networks for simpler tasks, they outperform them on more complex tasks, especially when dealing with top-k Singular Values. Additionally, the study demonstrates strong scalability, maintaining high accuracy even as matrix size increases. Furthermore, LLMs can achieve high accuracy with minimal prior examples, converging quickly and avoiding overfitting. These findings suggest that LLMs could provide an efficient alternative to classical methods for solving high-dimensional problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Large Language Models (LLMs) can solve tricky math problems without being explicitly trained on those problems. They focused on a specific type of math problem called Singular Value Decomposition, and the results show that LLMs are really good at it! In fact, they’re better than traditional methods for solving these types of problems when things get complicated. The paper also shows that LLMs can learn quickly and accurately with just a few examples, which is important because it means they won’t overfit to the training data. Overall, this study suggests that LLMs could be a useful tool for solving complex math problems in the future. |
Keywords
» Artificial intelligence » Linear regression » Overfitting
