Towards Intrinsic Interpretability of Large Language Models: A Survey of Design Principles and Architectures
Yutong Gao*, Qinglin Meng*, Yuan Zhou, Liangming Pan
CV
Education
- Ph.D. in Computer Science, Purdue University, 2022–2027 (expected)
- Advisor: Steve Hanneke
- B.S. in Mathematics, Tsinghua University, 2017–2022
Research
My research focuses on machine learning theory, reinforcement learning, and large language models. I am interested in the theoretical foundations of learning, including statistical learning theory, online learning, and algorithms with provable guarantees.
Publications
* Equal contribution
An Optimal Sauer Lemma Over ℓ-ary Alphabets
Steve Hanneke, Qinglin Meng, Shay Moran, Amirreza Shaeiri (alphabetical order)
Representation Preserving Multiclass Agnostic to Realizable Reduction
Steve Hanneke, Qinglin Meng, Amirreza Shaeiri (alphabetical order)
Learning Dynamic Mechanisms in Unknown Environments: A Reinforcement Learning Approach
Shuang Qiu*, Boxiang Lyu*, Qinglin Meng*, Zhaoran Wang, Zhuoran Yang, Michael I. Jordan