Special Topics in Large Language Models
CSE 5539 · Fall 2026 · The Ohio State University
Foundational homework released!
Foundational homework is released here. It will be due on Sept. 10 on Carmen.
This graduate level special topics course examines emerging frontiers in language modeling research. Specifically, this offering will focus on the following topics (subject to changes):
- Reasoning and Tool Use: Understanding model abilities and limitations in performing complex, multi-step inference.
- Long inputs and outputs: Analyzing model performance on long-context processing and long-form generation, including data requirements and efficiency considerations.
- New architectures and modeling paradigms, such as state space models (SSMs) and their variants, recurrent and linear-attention hybrids, and diffusion-based language models.
- Applications of language models for expert domains including law, medicine, journalism, and scientific discovery.
The format of the class will be a mix of lectures and research paper presentations. The course culminates in a semester long research or implementation project, presented as a final paper and in class presentation. Projects should emphasize novel failure modes, under explored behaviors, or emerging risks rather than incremental performance gains.
Prerequisite Knowledge
Students who participate in this class are expected to be self-motivated graduate students or senior undergraduate students.
Prerequisites: CSE 5525 is highly recommended; students must have experience with machine learning and deep learning including necessary mathematical background (i.e., they should have taken courses in linear algebra (Math 2568), multivariate calculus, probability, and statistics.). Some experience with natural language processing is required. We will touch upon the basics of language modeling and jump right into advanced topics.
Students should also feel comfortable with implementing machine learning algorithms and understanding/running open source machine learning code, specifically on GPUs, using libraries such as Pytorch or Jax.
Students should also have experience with reading machine learning papers and developing a decent understanding of the main concepts/ideas presented in the paper.
Note: Students who haven’t taken any of these courses but feel comfortable with deep learning and modern NLP, and students from other relevant departments such as statistics, linguistics, neuroscience and biomedical science are welcome to participate but please contact the instructor for approval.
Class Timing: Mondays at 12:40-2.30 pm
Class Location: DL 357
Code of Conduct
The strength of the university depends on academic and personal integrity. In this course, you must be honest and truthful, abiding by the University Academic Integrity Policy: https://oaa.osu.edu/academic-integrity-and-misconduct