Linguistic Generalization

Linguistic generalization in language models investigates how well these models can apply learned linguistic patterns to novel situations, going beyond simple memorization of training data. Current research focuses on evaluating this ability across various tasks (e.g., machine translation, semantic parsing) using different model architectures, such as Transformers and LSTMs, and exploring the role of factors like training data size and composition, multilingual training, and the incorporation of visual information. Understanding linguistic generalization is crucial for developing more robust and reliable language technologies, improving their ability to handle unseen data and reducing biases stemming from over-reliance on training set patterns.