Better Embeddings with Coupled Adam
Felix Stollenwerk, Tobias Stollenwerk
2025-02-18
Summary
This paper talks about a new way to improve how AI language models understand and represent words, called Coupled Adam. It's like giving the AI a better dictionary to work with, which helps it understand language more accurately.
What's the problem?
Current AI language models have a problem called anisotropy when they learn about words. This means the way they understand words is uneven or biased, which can make the AI less effective at language tasks. The researchers think this problem is caused by a part of the current learning method called Adam.
What's the solution?
The researchers created a modified version of Adam called Coupled Adam. This new method changes how the AI learns about words to reduce the anisotropy problem. They tested Coupled Adam and found that it significantly improves how well the AI understands and uses words, especially when working with large amounts of text data.
Why it matters?
This matters because better word understanding can make AI language models more accurate and useful for all kinds of tasks, from translation to writing assistance. By improving the foundation of how these AIs learn language, Coupled Adam could lead to smarter, more reliable AI systems that can help us in many areas of life and work.
Abstract
Despite their remarkable capabilities, LLMs learn word representations that exhibit the undesirable yet poorly understood feature of anisotropy. In this paper, we argue that the second moment in Adam is a cause of anisotropic embeddings, and suggest a modified optimizer called Coupled <PRE_TAG>Adam</POST_TAG> to mitigate the problem. Our experiments demonstrate that Coupled <PRE_TAG>Adam</POST_TAG> significantly improves the quality of embeddings, while also leading to better upstream and downstream performance on large enough datasets.