Скачать или смотреть Bridging Linguistic Theory and AI: Usage-Based Learning in Humans and Machines (COLING '25 Tutorial)

Full recording of the tutorial "Bridging Linguistic Theory and AI: Usage-Based Learning in Humans and Machines" from COLING 2025 in Abu Dhabi, UAE. Presented by Dr. Claire Bonial (DEVCOM US Army Research Lab), Dr. Harish Tayyar Madabushi (University of Bath), Dr. Nikhil Krishnaswamy (Colorado State University), and Dr. James Pustejovsky (Brandeis University). The tutorial website (including slide decks used) can be found at: (sites.google.com/view/linguistic-theory-and-ai/home)

Usage-based theories of human language, such as Construction Grammar, have been compelling theoretical lenses through which to view and evaluate what LLMs know and understand of language because of the parallels between usage-based learning and the data driven "learning'' of pre-trained models. However, a key difference between a usage-based learning account for humans and that of LLMs is in embodiment and multimodality---for the most part, LLMs use text alone, whereas usage-based theories posit that each token of linguistic experience is stored with a wealth of experiential information that enriches the symbol through cross-modal association. Therefore, the first goal of this tutorial is to provide a summary of language acquisition and second language learning from a usage-based theoretical linguistic perspective. With this understanding of human usage-based learning, we will turn to evidence demonstrating the ways in which machine learning, primarily via large, pre-trained vision and language models, does and does not parallel human learning. The overarching goal of this is not to say that the two processes are similar or dissimilar in order to conclude that dissimilarity denotes inferiority (if the knowledge arrived at is the same, then it may not matter how it was learned). Rather, we explore the resulting differences in what is known and understood about the world, and take this as a starting point for considering how to supplement and improve natural language understanding (NLU), particularly for physically situated applications.