Summary of Explaining Modern Gated-linear Rnns Via a Unified Implicit Attention Formulation, by Itamar Zimerman et al.
Explaining Modern Gated-Linear RNNs via a Unified Implicit Attention Formulation
by Itamar Zimerman, Ameen Ali, Lior Wolf
First submitted to arxiv on: 26 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 recent advancements in efficient sequence modeling have led to the development of attention-free layers such as Mamba, RWKV, and gated RNNs, which exhibit sub-quadratic complexity in sequence length and excellent scaling properties. This paper presents a unified view of these models by formulating them as implicit causal self-attention layers. The framework provides a direct means for applying explainability methods and compares the underlying mechanisms on similar grounds for different layers. Our experiments show that our attention matrices and attribution method outperform an alternative and a more limited formulation recently proposed for Mamba, while being effective and competitive in relevant metrics compared to state-of-the-art Transformer explainability methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary These recent advances in efficient sequence modeling have led to new types of foundation models. The paper takes a closer look at attention-free layers like Mamba, RWKV, and gated RNNs. It shows how these layers can be thought of as special kinds of self-attention layers. This helps us understand what’s going on inside the models and makes it easier to explain why they work the way they do. The research also compares different approaches to understanding these models and finds that its own method is better at explaining things than some other methods. |
Keywords
» Artificial intelligence » Attention » Self attention » Transformer
