Summary of Longssm: on the Length Extension Of State-space Models in Language Modelling, by Shida Wang
LongSSM: On the Length Extension of State-space Models in Language Modelling
by Shida Wang
First submitted to arxiv on: 4 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Dynamical Systems (math.DS)
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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 This paper explores the length-extension capabilities of state-space models (SSMs) in language modeling, focusing on the challenges and solutions when training models on short sequences and testing them on longer ones. The authors demonstrate that SSMs initialized with zero hidden states struggle with length extension, which is equivalent to polynomial extrapolation. To address this limitation, they propose a simple yet effective method: changing the hidden state initialization scheme. This approach enables efficient training of long-memory models using smaller training context lengths. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well state-space models can handle longer texts in language modeling. Right now, these models are good at understanding short texts but struggle with longer ones. The researchers figure out why this is happening and then come up with a simple solution to make the models better. They show that using shorter training sequences doesn’t always mean you’ll get worse results. By changing how they start their calculations, they can train models that understand long texts without needing super-long training. |
