Keskeiset käsitteet
The author explores the generalization capacity of neural networks in multimodal reasoning, highlighting the importance of cross-attention mechanisms for improved performance.
Tiivistelmä
The study evaluates neural network architectures for multimodal reasoning generalization. Models with cross-attention mechanisms excel in OOD distractor and systematic generalization but struggle with productive compositional generalization. Increasing layer depth enhances systematic and distractor generalization but has limited impact on productivity.
The research introduces a benchmark, gCOG, to assess multimodal reasoning. Results indicate that purely neural models face challenges in productive compositional generalization compared to hybrid neuro-symbolic approaches. The study emphasizes the need for neural architectures capable of robust multimodal OOD generalization.
Tilastot
Models with cross-attention mechanisms exhibit excellent OOD distractor and systematic generalization.
All models fail to perform OOD productive compositional generalization.
Increasing encoder layers improves generalization across distractor and systematic tasks.