Summary of Provable Length Generalization in Sequence Prediction Via Spectral Filtering, by Annie Marsden et al.
Provable Length Generalization in Sequence Prediction via Spectral Filtering
by Annie Marsden, Evan Dogariu, Naman Agarwal, Xinyi Chen, Daniel Suo, Elad Hazan
First submitted to arxiv on: 1 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 investigates the problem of length generalization in sequence prediction, proposing a new metric called Asymmetric-Regret to measure performance against a benchmark predictor with longer context length. The authors then focus on the spectral filtering algorithm, developing a gradient-based learning algorithm that achieves length generalization for linear dynamical systems. Experiments confirm theoretical predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how machines can predict sequences of things when they have more information than we do. They create a new way to measure how well this works called Asymmetric-Regret. Then, they use an algorithm that filters out noise and helps the machine make better predictions. They test this with some examples. |
Keywords
» Artificial intelligence » Context length » Generalization
