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Better Embeddings with Couple Adam

작성자
  • Haebom

Author

Felix Stollenwerk, Tobias Stollenwerk

Outline

This paper addresses the problem of large-scale language models (LLMs) learning word representations with an undesirable property called anisotropy. The researchers argue that the second moment of the Adam optimizer is the cause of the anisotropic embeddings and propose a modified optimizer, Coupled Adam, to mitigate this problem. Experimental results show that Coupled Adam significantly improves the quality of embeddings and enhances the performance of both superordinate and subordinate tasks on sufficiently large datasets.

Takeaways, Limitations

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Takeaways:
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We suggest that the second moment of the Adam optimizer may be the cause of the anisotropic embedding problem in LLM.
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We show that a new optimizer called Coupled Adam can alleviate the anisotropy problem and improve the performance of LLM.
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It contributes to improving the performance of upper and lower tasks on sufficiently large datasets.
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Limitations:
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The effectiveness of the proposed Coupled Adam may depend on the dataset size (it has been shown to be effective on large datasets, but its effectiveness may vary on smaller datasets).
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This may not be a complete solution to the root cause of the anisotropy problem (we only addressed the second moment, and there may be other factors involved).
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