Scale leads to compositional generalization
Florian Redhardt, Yassir Akram, Simon Schug
Can neural networks systematically capture discrete, compositional task structure despite their continuous, distributed nature?The impressive capabilities of large-scale neural networks suggest that the answer to this question is yes.However, even for the most capable models, there are still frequent failure cases that raise doubts about their systematicity.Here, we seek to understand what it takes for a standard neural network to generalize over tasks that share compositional structure.We find that simply scaling data and model size leads to compositional generalization.We show that this holds across different task encodings as long as the training distribution sufficiently covers the task space.In line with this finding, we prove that standard multilayer perceptrons can approximate a general class of compositional task families to arbitrary precision using only a linear number of neurons with respect to the number of task modules.Finally, we uncover that if networks successfully compositionally generalize, the constituents of a task can be linearly decoded from their hidden activations.We show that this simple metric correlates with instances where text-to-image generation models fail to compose known concepts.