Summary of Xlstm: Extended Long Short-term Memory, by Maximilian Beck et al.
xLSTM: Extended Long Short-Term Memory
by Maximilian Beck, Korbinian Pöppel, Markus Spanring, Andreas Auer, Oleksandra Prudnikova, Michael Kopp, Günter Klambauer, Johannes Brandstetter, Sepp Hochreiter
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (stat.ML)
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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 explores the potential of Long Short-Term Memory (LSTM) models in language modeling by scaling them up to billions of parameters and incorporating modern techniques from Large Language Models (LLMs). The authors introduce exponential gating with normalization and stabilization techniques, and modify the LSTM memory structure to create scalar and matrix-based variants. These extensions, called sLSTM and mLSTM, are integrated into residual block backbones to form xLSTM blocks that can be stacked into architectures. Compared to state-of-the-art Transformers and State Space Models, xLSTMs show improved performance and scalability. This work aims to revitalize the use of LSTMs in language modeling, leveraging their strengths while mitigating known limitations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well an old type of AI model called Long Short-Term Memory (LSTM) can be used for natural language processing tasks like language modeling. The authors want to see if they can make LSTMs work better by adding new techniques and scaling them up to use a lot more data and parameters. They come up with some new ideas, like using exponential gating and changing the way LSTMs store and update information. When they test these new models, they find that they perform well compared to other state-of-the-art models. This research tries to show that LSTMs can still be useful for language modeling tasks, even in an era where newer models have become popular. |
Keywords
» Artificial intelligence » Lstm » Natural language processing
