Summary of Unlocking State-tracking in Linear Rnns Through Negative Eigenvalues, by Riccardo Grazzi et al.
Unlocking State-Tracking in Linear RNNs Through Negative Eigenvalues
by Riccardo Grazzi, Julien Siems, Arber Zela, Jörg K.H. Franke, Frank Hutter, Massimiliano Pontil
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Formal Languages and Automata Theory (cs.FL)
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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 investigates the limitations of Linear Recurrent Neural Networks (LRNNs) in performing state-tracking, a crucial task for applications like code evaluation. Current architectures struggle to solve even parity, a simple state-tracking problem, whereas non-linear RNNs can handle it effectively. The authors identify that restricting the value range of LRNN diagonal state-transition matrices to [0, 1] is the main cause of this limitation and propose extending the eigenvalue range to include negative values. This modification enables LRNNs like Mamba and DeltaNet to solve parity and consistently improves their performance on state-tracking tasks. The paper also demonstrates that state-tracking enabled LRNNs can be pre-trained stably and efficiently at scale, achieving competitive performance on language modeling and showing promise on code and math tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how Linear Recurrent Neural Networks (LRNNs) work with long sequences of data. Right now, both LRNNs and a different type of network called Transformers have trouble keeping track of the state of something. This is important for tasks like evaluating code written in a programming language. The researchers found that the main problem is that LRNNs are limited by only being able to use values between 0 and 1 when changing from one state to another. By allowing these networks to use negative values too, they can solve this problem and do better at keeping track of states. |
Keywords
» Artificial intelligence » Tracking
