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Neural networks have revolutionized language modeling and excelled in variousdownstream tasks. However, the extent to which these models achievecompositional generalization comparable to human cognitive abilities remains atopic of debate. While existing approaches in the field have mainly focused onnovel architectures and alternative learning paradigms, we introduce apioneering method harnessing the power of dataset cartography (Swayamdipta etal., 2020). By strategically identifying a subset of compositionalgeneralization data using this approach, we achieve a remarkable improvement inmodel accuracy, yielding enhancements of up to 10% on CFQ and COGS datasets.Notably, our technique incorporates dataset cartography as a curriculumlearning criterion, eliminating the need for hyperparameter tuning whileconsistently achieving superior performance. Our findings highlight theuntapped potential of dataset cartography in unleashing the full capabilitiesof compositional generalization within Transformer models. Our code isavailable at https://github.com/cyberiada/cartography-for-compositionality.

Harnessing Dataset Cartography for Improved Compositional Generalization in Transformers