Summary of Your Transformer Is Secretly Linear, by Anton Razzhigaev et al.
Your Transformer is Secretly Linear
by Anton Razzhigaev, Matvey Mikhalchuk, Elizaveta Goncharova, Nikolai Gerasimenko, Ivan Oseledets, Denis Dimitrov, Andrey Kuznetsov
First submitted to arxiv on: 19 May 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 novel linear characteristic found in transformer decoders, including GPT, LLaMA, OPT, BLOOM, and others, is analyzed in this paper. The study reveals a near-perfect linear relationship (Procrustes similarity score of 0.99) between sequential layers when the residual component is included. However, removing or approximating these linear blocks does not significantly affect model performance. To reduce layer linearity, a cosine-similarity-based regularization is introduced in pretraining experiments on smaller models. This regularization improves performance metrics on benchmarks like Tiny Stories and SuperGLUE while decreasing model linearity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Transformers are super cool machines that help us understand language. Scientists found out that some parts of transformers have a special linear pattern, which helps them work well. They tested this idea by looking at how different models worked when they didn’t use this pattern. Surprisingly, it didn’t make much difference! To make the models even better, they tried a new trick called cosine-similarity-based regularization. This made the models perform really well on some tasks and made them less linear, which is interesting because we thought transformers were more complicated than that. |
Keywords
» Artificial intelligence » Cosine similarity » Gpt » Llama » Pretraining » Regularization » Transformer
