Events

Faculty Candidate Talk Learning to Reason with LLMs
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Speaker: Xiang Yue

Bio

Xiang Yue is a Postdoctoral Fellow at Carnegie Mellon University. He received his PhD from The Ohio State University in 2023. His research focuses on understanding and enhancing the reasoning capabilities of large language models. He has been awarded a postdoctoral fellowship from the Carnegie Bosch Institute, two AI rising stars, two Best Paper Finalist or Honorable Mention at CVPR 2024 and ACL 2023. Xiang’s recent work on developing the MMMU evaluation benchmark has garnered attention beyond academia, being featured in the releases of OpenAI GPT-4o and Google Gemini.

Location : CoRE 301

Committee: 

Event Type: Faculty Candidate Talk

Abstract: Large language models (LLMs) have achieved impressive progress, yet major challenges remain in enhancing their reasoning capabilities for complex tasks. In this talk, I will present our recent work on understanding and improving LLM reasoning. I will begin by discussing our efforts of understanding the reasoning, including the development of widely-used reasoning benchmarks such as MMMU and MMLU-Pro, and our studies on key factors that impact LLM reasoning performance. I will then describe our approach to improving reasoning abilities by generating large-scale synthetic reasoning data and shaping reward functions within reinforcement learning frameworks to better train reasoning models. I will conclude with a discussion on promising future directions, including how models can learn to reason more effectively through exploration and feedback.

Organization: 

Contact  Professor Yongfeng Zhang

Join Zoom Meeting
https://rutgers.zoom.us/j/2014444359?pwd=WW9ybFNCNVFrUWlycHowSHdNZjhzUT09

Meeting ID: 201 444 4359
Password: 550978