Summary of Modularity in Transformers: Investigating Neuron Separability & Specialization, by Nicholas Pochinkov et al.
Modularity in Transformers: Investigating Neuron Separability & Specialization
by Nicholas Pochinkov, Thomas Jones, Mohammed Rashidur Rahman
First submitted to arxiv on: 30 Aug 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 Transformer models are widely used in various applications, but their internal workings remain unclear. This paper explores the modularity and task specialization of neurons within transformer architectures, focusing on vision (ViT) and language (Mistral 7B) models. The authors analyze neuron overlap and specialization across different tasks and data subsets using pruning and MoEfication clustering techniques. Their findings show that neurons develop task-specific patterns, with varying degrees of overlap between related tasks. Neuron importance patterns persist even in randomly initialized models, suggesting an inherent structure refined by training. Additionally, the authors find that MoEfication-identified neuron clusters correspond more strongly to task-specific neurons in earlier and later layers of the models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformer models are very good at doing many things, but we don’t fully understand how they work inside. This study looks at what happens when you use these models for different tasks, like recognizing pictures or understanding language. The researchers used special tools to see which parts of the model were important and what was happening in each part. They found that different parts of the model get better at doing specific things as it’s trained. Some parts work together well, while others do their own thing. This helps us understand these models better and might even help make them more efficient. |
Keywords
» Artificial intelligence » Clustering » Pruning » Transformer » Vit
