Summary of Transformer Block Coupling and Its Correlation with Generalization in Llms, by Murdock Aubry et al.
Transformer Block Coupling and its Correlation with Generalization in LLMs
by Murdock Aubry, Haoming Meng, Anton Sugolov, Vardan Papyan
First submitted to arxiv on: 10 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 analyzes the internal mechanics of Large Language Models (LLMs) by examining the trajectories of token embeddings as they pass through transformer blocks. By linearizing the system using Jacobian matrices, the authors uncover a phenomenon called “transformer block coupling,” where top singular vectors across tokens and depth become correlated. This coupling is found to positively correlate with model performance, stronger than other hyperparameters such as parameter count or embedding dimension. The study also investigates how this property emerges during training, observing progressive development of coupling and increased linearity. Additionally, experiments with Vision Transformers (ViTs) confirm the emergence of coupling and its relationship with generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies how Large Language Models work inside. They look at how token embeddings move through transformer blocks and find a special connection between them called “transformer block coupling.” This connection makes models better. The study shows that this connection is important, even more important than other things like how many parameters or how deep the model is. The researchers also see how this connection grows during training and becomes stronger. |
Keywords
* Artificial intelligence * Embedding * Generalization * Token * Transformer
