Optimizing for Long-Term Vision in a Fast-Paced Research World

Abstract: The fast-paced race for larger language models—and the promise of financial gains for the winners—incentivizes heavier engineering with incremental ideas, often at the expense of long-term vision. While this approach advances industry products used by millions, it is not necessarily the right approach for academic research. In this talk, I will present novel task formulations and evaluation benchmarks that question mainstream assumptions about LLM architectures, training/alignment algorithms, and evaluation approaches. While proposed ideas contradict the common practice, they expose blind spots in LLMs reasoning abilities, and huge performance and fairness gaps in best commercial LLMs, highlighting directions for future research.

Bio: Yulia Tsvetkov is an associate professor at the Paul G. Allen School of Computer Science & Engineering at University of Washington. Her research group works on fundamental advancements to large language models, multilingual NLP, and AI ethics/safety. This research is motivated by a unified goal: to extend the capabilities of human language technology beyond individual populations and across language boundaries, thereby making NLP tools available to all users. Prior to joining UW, Yulia was an assistant professor at Carnegie Mellon University and before that a postdoc at Stanford. Yulia is a recipient of NSF CAREER, Sloan Fellowship, Okawa Research award, and multiple paper awards and runner-ups at NLP, ML, and CSS conferences.