Summary of When Can Transformers Compositionally Generalize In-context?, by Seijin Kobayashi et al.
When can transformers compositionally generalize in-context?
by Seijin Kobayashi, Simon Schug, Yassir Akram, Florian Redhardt, Johannes von Oswald, Razvan Pascanu, Guillaume Lajoie, João Sacramento
First submitted to arxiv on: 17 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Neural and Evolutionary Computing (cs.NE)
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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 compositional generalization abilities of transformers in a modular multitask setting, where tasks can be composed from independent components. The authors investigate under what conditions transformers can generalize from a subset of tasks to all possible combinations that share similar components. They find that transformers struggle to generalize compositionally despite being theoretically capable of doing so. Instead, introducing a bottleneck that separates task inference and execution enables compositional generalization. This study sheds light on the limitations and possibilities of transformer-based models in learning complex compositional structures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have many puzzles to solve, each with its own unique pieces. Can a computer learn to solve all these puzzles by just seeing some of them? Researchers studied how well computers (called transformers) can do this. They found that the computers are good at solving individual puzzles but struggle to figure out new puzzles if they haven’t seen similar ones before. However, when they added a special “filter” that helps the computer focus on the right pieces, it became much better at solving all kinds of puzzles. |
Keywords
» Artificial intelligence » Generalization » Inference » Transformer
