Subject Verb Agreement

Subject-verb agreement, the grammatical rule aligning verb form with subject number and person, is a key area of research in computational linguistics, focusing on how well language models capture and utilize this syntactic information. Current research investigates the extent to which various neural network architectures, particularly transformer-based models like BERT, accurately perform subject-verb agreement, even in complex sentences with intervening phrases ("attractors") or across different languages. These studies reveal limitations in models' ability to generalize this knowledge beyond training data, highlighting the need for improved understanding of how these models represent and process syntactic structure. This work has implications for improving natural language processing applications, such as machine translation and text generation, by enhancing grammatical accuracy and robustness.