11-811: Interdisciplinary NLP: Language Modeling in the Wild

Fall 2026  ·  Tuesdays & Thursdays, 12:30–1:50pm  ·  Wean Hall 6403

Instructors: Emma Strubell and Clara Na

Overview

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.

Logistics

This class is likely a good fit for you if either of the following descriptions apply to you.

Group A: You are a student “in NLP” – i.e. actively engaged in NLP research through LTI faculty and/or coursework or similar, and interested in any or all of: 1) interdisciplinary research and communication, 2) domain adaptation and generalization, especially in practice, and 3) understanding common gaps between research and practice. You do not need to have a specific domain of interest yet, but you should be open to working with domain experts to accomplish a shared goal.

Group B: You are a student in a discipline outside of ML/NLP (e.g. a sufficiently different discipline within computing, or an entirely separate discipline such as English, biology or design), and you are interested in using language technologies (e.g. machine learning with text data, LLMs) for your work. You do not need to have a specific use case yet – part of the course’s objective will be to refine a research question in the context of available resources and technology – but you should have an understanding in general of what it looks like to do research in your discipline

A detailed syllabus and schedule are forthcoming and will be posted to this page. Please feel free to reach out to the instructors with any questions in the meantime!

Schedule

Dates are tentative and subject to change. Readings and materials will be posted as the semester progresses.

Grading

Policies

Late Work: TBD

Academic Integrity: TBD

Accommodations: Students with disabilities who require accommodations should contact the Office of Disability Resources and notify the instructors early in the semester.

Wellness: Take care of yourself. CMU offers support through Counseling & Psychological Services (CaPS).