Summary of On Provable Length and Compositional Generalization, by Kartik Ahuja et al.
On Provable Length and Compositional Generalization
by Kartik Ahuja, Amin Mansouri
First submitted to arxiv on: 7 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); 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 A novel paper in sequence-to-sequence modeling explores the out-of-distribution capabilities of popular architectures like deep sets, transformers, state space models, and recurrent neural nets. The study delves into two key aspects: length generalization (handling longer sequences than trained on) and compositional generalization (generalizing to unseen token combinations). The paper provides first-of-its-kind guarantees for these models’ ability to generalize when trained to minimize prediction error. It finds that limited capacity variants of these architectures achieve both types of generalization, provided the training data is diverse enough. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us understand how sequence-to-sequence models can work well even on new situations they haven’t seen before. It looks at four different types of models and shows that if we make them a bit simpler (limited capacity), they can do two important things: handle longer sequences than they were trained on, and also be able to recognize combinations of words or phrases that weren’t in their training data. |
Keywords
* Artificial intelligence * Generalization * Token
