Lexical Inference

Lexical inference focuses on how humans and computational models understand and reason with the meanings of words, particularly in the context of larger sentences or texts. Current research investigates how neural network models, such as BERT and T5, learn to perform lexical inference, often focusing on the role of exemplar-based learning and the development of controlled inference architectures to improve explainability and robustness. This work is significant because it advances our understanding of both human language processing and the capabilities and limitations of artificial intelligence, with potential applications in natural language understanding, commonsense reasoning, and the development of more reliable and interpretable AI systems.