Representational Homomorphism Predicts and Improves Compositional Generalization In Transformer Language Model

1. 1Define Homomorphism Error (HE) as a structural metric to measure inconsistency between established rules
2. 2Apply HE to evaluate the compositional generalization of Transformer language models
3. 3Use HE to identify and improve representational inconsistencies in models
4. 4Integrate HE into the training process to enhance model performance

ML researchers and AI engineers benefit from this research as it provides insight into why models fail at the representational level, allowing them to improve model performance

💡 Homomorphism Error (HE) measures inconsistency between established rules, improving model performance

💡 Representational Homomorphism improves compositional generalization in Transformers