AESLA 2024

Laurence Anthony es catedrático de Lingüística Aplicada en la Facultad de Ciencias e Ingeniería de la Universidad de Waseda (Japón). Es licenciado en Física Matemática por la Universidad de Manchester (Reino Unido) y posee un máster (TESL/TEFL) y un doctorado (Lingüística Aplicada) por la Universidad de Birmingham (Reino Unido). Es miembro fundador del Center for English Language Education in Science and Engineering (CELESE), donde imparte cursos de idiomas de disciplinas específicas a los 10.000 estudiantes de la facultad. Sus principales intereses de investigación se centran en la lingüística de corpus, la tecnología educativa y el diseño de programas y metodologías de enseñanza del inglés con fines específicos (ESP). Recibió el Premio nacional de la Japan Association for English Corpus Studies (JAECS) en 2012 por su trabajo en el diseño de herramientas de software para corpus, incluida la creación de AntConc.

Understanding and explaining discourse with the help of Large Language Models

Recent developments in Artificial Intelligence (AI) have taken the world by storm. The ChatGPT large language model (LLM), for example, is reported to be the fastest-growing Internet service of all time, and each day we read reports of new models and innovations in AI that affect our daily lives. The impact of AI on applied linguistics research and language teaching is also expected to be unprecedented. Even within a year of the release of ChatGPT, we have already seen dramatic changes in the way researchers conduct studies, teachers prepare materials for class, and learners complete their class reports. In this paper, I will first introduce some of the key building blocks of LLMs and explain how their design not only leads to some very interesting and powerful emergent properties, but also a few important weaknesses. Next, I will show how LLMs can facilitate a new age of discourse-related research by assisting in the annotation of textual data during corpus creation. Then, I will show how LLMs can assist in the preparation of classroom materials that help learners to better understand the form and function of discourse structures through example generation and analysis. Finally, I will discuss some important directions for discourse-related research that will allow applied linguists to play an important role in the development of future LLMs and AI systems.