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arXiv · 2407.12275

When can transformers compositionally generalize in-context?

Seijin Kobayashi·Simon Schug·Yassir Akram·Florian Redhardt·Johannes von Oswald·Razvan Pascanu·Guillaume Lajoie·João Sacramento
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Abstract

Many tasks can be composed from a few independent components. This gives rise to a combinatorial explosion of possible tasks, only some of which might be encountered during training. Under what circumstances can transformers compositionally generalize from a subset of tasks to all possible combinations of tasks that share similar components? Here we study a modular multitask setting that allows us to precisely control compositional structure in the data generation process. We present evidence that transformers learning in-context struggle to generalize compositionally on this task despite being in principle expressive enough to do so. Compositional generalization becomes possible only when introducing a bottleneck that enforces an explicit separation between task inference and task execution.

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Seijin Kobayashi, Simon Schug, Yassir Akram, Florian Redhardt, Johannes von Oswald, Razvan Pascanu, Guillaume Lajoie, João Sacramento. 2024-07-17. When can transformers compositionally generalize in-context?. https://arxiv.org/abs/2407.12275

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