Dataset diversity, not architecture, drives Transformer generalization gaps
Researchers challenge a widely held assumption about Transformer limitations by demonstrating that structural generalization failures stem not from architectural constraints but from training data imbalance. By systematically varying the number of distinct structural and lexical patterns in benchmark datasets like COGS and SLOG, the team shows Transformers can match lexical generalization performance when given equivalent type diversity. This finding reshapes how the field should interpret compositional generalization benchmarks and suggests practitioners may need to reconsider dataset construction rather than model design when addressing generalization gaps.
Modelwire context
ExplainerThe paper's real contribution is narrower than it first appears: it shows that when you match the number of distinct types (grammatical patterns and word forms) across datasets, Transformers stop failing at compositional tasks. This doesn't prove Transformers are compositionally competent in general; it proves they're sensitive to type distribution in a way prior work didn't isolate.
This is largely disconnected from recent activity in the space, as there is no prior Modelwire coverage on compositional generalization benchmarks or Transformer architectural limits. The finding belongs to a longer-running debate about whether Transformers fundamentally struggle with systematic generalization or whether benchmark design has been misleading the field. The COGS and SLOG datasets mentioned here are standard evaluation tools in this subfield, so this work directly challenges how researchers should interpret failures on those benchmarks going forward.
If the same type-diversity effect holds when researchers test on held-out grammatical structures (not just lexical items) using the Grammatical Framework, that confirms the finding generalizes beyond these two benchmarks. If it doesn't, the result may be specific to how COGS and SLOG distribute their types, and the architectural debate remains unresolved.
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MentionsTransformers · COGS dataset · SLOG dataset · Grammatical Framework
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