Marco Baroni

Fodor and Pylyshyn's systematicity challenge to neural networks remains unmet, paper argues

A new arXiv preprint argues that recent claims that neural networks have met Fodor and Pylyshyn's systematicity challenge are premature. The authors specifically target Lake and Baroni's meta-learning for compositionality (MLC) protocol, showing it struggles with out-of-distribution rules and behaves unsystematically on many within-distribution problems. The paper concludes that the classical cognitive science challenge — that neural networks cannot explain systematic biconditional dependencies in language and thought — remains unresolved.