Summary of Stuffed Mamba: State Collapse and State Capacity Of Rnn-based Long-context Modeling, by Yingfa Chen et al.
Stuffed Mamba: State Collapse and State Capacity of RNN-Based Long-Context Modeling
by Yingfa Chen, Xinrong Zhang, Shengding Hu, Xu Han, Zhiyuan Liu, Maosong Sun
First submitted to arxiv on: 9 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 A novel paper investigates the limitations of recurrent neural networks (RNNs) in handling long sequences during inference, particularly their inability to extrapolate to inputs longer than the training length. The study identifies “state collapse” as the primary cause of performance degradation on unseen sequence lengths due to overfitting caused by the recurrent state being overparameterized for the training length. To address this issue, three mitigation methods are proposed to improve Mamba-2’s length generalizability, enabling it to process more than 1M tokens without “state collapse”. The research also explores the recurrent state capacity in language modeling and passkey retrieval, finding that it scales exponentially with the state size. This study holds promise for RNN-based long-context modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Recurrent neural networks (RNNs) are fast at handling long sequences, but they struggle when dealing with longer contexts. Researchers have noticed this problem but haven’t fully understood why it happens. They found that if you train an RNN on short sequences, it won’t be good at understanding longer texts. This is because the RNN’s internal state becomes too complex and gets stuck in a simple pattern, which hurts its performance. To fix this, scientists developed three new techniques to help RNNs learn from long texts without getting stuck. These methods allowed an RNN to understand text over 1 million words long! The study also showed that the more powerful the RNN, the better it is at handling longer texts. |
Keywords
» Artificial intelligence » Inference » Overfitting » Rnn
