Summary of Can Mamba Learn How to Learn? a Comparative Study on In-context Learning Tasks, by Jongho Park et al.
Can Mamba Learn How to Learn? A Comparative Study on In-Context Learning Tasks
by Jongho Park, Jaeseung Park, Zheyang Xiong, Nayoung Lee, Jaewoong Cho, Samet Oymak, Kangwook Lee, Dimitris Papailiopoulos
First submitted to arxiv on: 6 Feb 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 abstract presents research on state-space models (SSMs) and their in-context learning (ICL) capabilities compared to Transformer networks. The study evaluates the performance of SSMs, specifically Mamba, against Transformers across various tasks. Results show that SSMs perform similarly to Transformers in standard regression ICL tasks but outperform them in sparse parity learning. However, SSMs struggle with non-standard retrieval functionality. To address this, a hybrid model, MambaFormer, is introduced, combining Mamba with attention blocks and surpassing individual models in challenging tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study explores the abilities of state-space models (SSMs) to learn without optimizing parameters, just like Transformer networks can. The researchers tested SSMs, particularly Mamba, against Transformers on different tasks. They found that SSMs are good at some tasks but not others. To make them better, they created a new model called MambaFormer that combines the strengths of both. |
Keywords
* Artificial intelligence * Attention * Regression * Transformer
