Summary of Smr: State Memory Replay For Long Sequence Modeling, by Biqing Qi et al.
SMR: State Memory Replay for Long Sequence Modeling
by Biqing Qi, Junqi Gao, Kaiyan Zhang, Dong Li, Jianxing Liu, Ligang Wu, Bowen Zhou
First submitted to arxiv on: 27 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel strategy to address limitations in state space models (SSMs) that hinder efficient computation via convolution. The authors identify the Non-Stable State (NSS) problem, where deviations from sampling point requirements lead to error transmission and accumulation, causing divergence of the SSM’s hidden state. They introduce Sampling Step Adaptation (SSA) by adjusting input sequences with early memories to mitigate this issue. Furthermore, they introduce a plug-and-play mechanism, State Memory Replay (SMR), which utilizes learnable memories to adjust the current state with multi-step information for generalization at sampling points different from those in the training data. This enables SSMs to stably model varying sampling points. The proposed method is evaluated on long-range modeling tasks in autoregressive language modeling and Long Range Arena, demonstrating its effectiveness for a series of SSM models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves problems with state space models (SSMs) that make them hard to use. The authors found that when we don’t get the right data points, our model gets mixed up and stops working well. They developed a new way to fix this problem by adjusting what we give the model to start with. This helps the model work better even when the data is different from what it learned on. They tested their idea and showed that it makes state space models work better for tasks like language modeling. |
Keywords
» Artificial intelligence » Autoregressive » Generalization
