Interdisciplinary NLP: Language Modeling in the Wild

Catalog description

Recent advances in natural language processing (NLP), primarily powered by large language models (LLMs) show great potential for enabling advanced analysis of unstructured and semi-structured documents across a diverse array of applications — from accelerating scientific discovery by automatically analyzing materials science research literature, to facilitating a study of the evolution of narrative arcs in 20th century literature. Historically, successful real world deployment has often required deliberate adaptation: careful definition of the task, curation of new or existing datasets, experimentation to identify strengths and limitations of existing off-the-shelf affordances, and/or consideration of computational and financial feasibility. On the other hand, recent developments in language technologies have included both 1) meaningful capability improvements in many settings that until recently were outside the scope of existing tools, and 2) lowered barriers to use and adaptation of language technologies. In this class, students with concentrations outside of NLP (e.g. degree programs in materials science, English, ...) and students with concentrations in or near NLP (LTI, MLD or equivalent expertise) will work with and learn from each other, to characterize and bridge gaps between the promise of modern language technologies and the successful deployment of these tools for real-world applications. Together, students will explore: Technical foundations for using language technologies, AI literacy and effective science communication; Identifying strengths and limitations of various approaches for adaptation to a specific domain or setting, and; Acquiring and curating data appropriate to a specific task or evaluation; Devising and executing a plan to accomplish research and analysis tasks given a goal Course Website: https://strubell.github.io/teaching/11-811/