4 papers
Neural Language Models Learn the Contextual Distributions of Dependency Structures: a statistical learning theory to compositionality
Wang Bojun, Junjie Chen, Holly Jenkins +1
It is unclear how Neural Language Models (NLMs) acquire the structural meaning encoded by grammatical structures that is independent of lexical semantics. We propose a statistical…
Generalization and Memorization along the Learning Trajectory of Neural Language Models: A Geometric Account of Categorization
Wang Bojun, Holly Jenkins, Elizabeth Wonnacott
We investigate how generalization and memorization develop along the learning trajectory of neural language models. Using controlled synthetic grammars, we examine both the geometr…
Developmental approach reveals the statistical learning of Neural Language Models: Transformers generalize from the most abstract statistical patterns
Wang Bojun, Holly Jenkins, Elizabeth Wonnacott
In this study, we use a developmental approach to investigate the statistical learning and mental representation of neural language models (NLM). A series of Generative Transformer…
Towards an Evaluation Methodology for AI in Second Language Education: Lessons Learned from Developing L2-Bench
James Edgell, Wm. Matthew Kennedy, Isaac Pattis +3
The rapid adoption of large language models in AI-powered language education has created an urgent need for evaluations that assess pedagogical effectiveness, particularly in langu…