Summary of How Well Can a Long Sequence Model Model Long Sequences? Comparing Architechtural Inductive Biases on Long-context Abilities, by Jerry Huang
How Well Can a Long Sequence Model Model Long Sequences? Comparing Architechtural Inductive Biases on Long-Context Abilities
by Jerry Huang
First submitted to arxiv on: 11 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 challenges of modeling long sequences in real-world scenarios. While recent advances in deep neural networks have enabled scaling up to support extended context lengths, the authors question whether these claims hold true in practice. They evaluate recurrent and linear recurrent neural network models, finding that they still struggle with long contexts, despite theoretical claims of infinite sequence length. The paper highlights the need for further study into the inconsistent extrapolation capabilities of different inductive biases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to model really long sequences that happen in real life. Right now, deep learning models are struggling to handle these long sequences because they get stuck on short-term details and can’t see the bigger picture. The researchers want to know if some new kinds of neural networks called recurrent and linear recurrent neural networks can actually handle really long sequences like we need them to. They tested these models and found that even though they’re good in theory, they still have a hard time handling long contexts. This means we need to keep working on making these models better so they can help us with things we want to do. |
Keywords
» Artificial intelligence » Deep learning » Neural network
