I am a Ph.D. student in Computer Science at UCSD advised by Jingbo Shang. Previously, I was an AI resident at Google Research. I received my B.A. in Computational Linguistics from Peking University.

My research interests are interdisciplinary, including (1) using computational methods to investigate fundamental linguistic questions and (2) applying findings from linguistics to develop techniques for automatic natural language understanding. I am honored to be selected as a DeepMind Scholar.

Education

• University of California, San Diego
  Sep. 2021 - Present
  Ph.D. in Computer Science

• The Department of Chinese Language and Literature, Peking University
  Sept. 2015 - Jul. 2019
  B.A. in Computational Linguistics

Experience

• Natural Language Understanding Team, Google Research
  Jun. 2022 - Sep. 2022
  Student Researcher

• Natural Language Understanding Team, Google Research
  Oct. 2019 - Sep. 2021
  AI Resident

• Knowledge Computing Group, Microsoft Research Asia
  Apr. 2019 - Jul. 2019
  Research Intern

• Chinese Language Processing Group, Computer Science Department, Brandeis University
  Jul. 2018 - Sept. 2018
  Research Assistant

• Key Laboratory of Computational Linguistics, Peking University
  Jul. 2016 - Feb. 2018
  Research Assistant

Selected Publication

• Retrieval-Augmented Parsing for Complex Graphs by Exploiting Structure and Uncertainty
  Zi Lin, Quan Yuan, Panupong Pasupat, Jeremiah Zhe Liu, Jingbo Shang
  EMNLP findings 2023
  Paper | Code

• ToxicChat: Unveiling Hidden Challenges of Toxicity Detection in Real-World User-AI Conversation
  Zi Lin*, Zihan Wang*, Yongqi Tong, Yangkun Wang, Yuxin Guo, Yujia Wang, Jingbo Shang
  EMNLP findings 2023
  Arxiv | Blog | Data

• Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90% ChatGPT Quality
  Wei-Lin Chiang*, Zhuohan Li*, Zi Lin*, Ying Sheng*, Zhanghao Wu*, Hao Zhang*, Lianmin Zheng*, Siyuan Zhuang*, Yonghao Zhuang*, Joseph E. Gonzalez, Ion Stoica, Eric P. Xing
  Blogpost 2023
  Blog | Demo | Code

• On Compositional Uncertainty Quantification for Seq2seq Graph Parsing
  Zi Lin, Du Phan, Panupong Pasupat, Jeremiah Liu, Jingbo Shang
  ICLR 2023
  Paper | Code

• A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
  Jeremiah Liu, Shreyas Padhy, Jie Ren, Zi Lin, Yeming Wen, Ghassen Jerfel, Zachary Nado, Jasper Snoek, Dustin Tran, Balaji Lakshminarayanan
  JMLR 2023
  Paper | Arxiv | Code

• Neural-Symbolic Inference for Robust Autoregressive Graph Parsing via Compositional Uncertainty Quantification
  Zi Lin, Jeremiah Liu, Jingbo Shang
  EMNLP 2022
  Paper | Arxiv | Code

• Towards Collaborative Neural-Symbolic Graph Semantic Parsing via Uncertainty
  Zi Lin, Jeremiah Liu, Jingbo Shang
  ACL Findings 2022
  Paper | Slides

• Measuring and Improving Model-Moderator Collaboration using Uncertainty Estimation
  Ian D. Kivlichan*, Zi Lin*, Jeremiah Liu*, Lucy Vasserman
  ACL 2021 workshop on Online Abuse and Harms (WOAH)
  Paper | Slides | Arxiv | Code

• Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
  Jeremiah Liu, Zi Lin, Shreyas Padhy, Dustin Tran, Tania Bedrax-Weiss, Balaji Lakshminarayanan
  Neurips 2020
  Paper | Poster | Arxiv | Code

• Comparing Knowledge-Intensive and Data-Intensive Models for English Resource Semantic Parsing
  Junjie Cao*, Zi Lin*, Weiwei Sun, Xiaojun Wan
  Computational Linguistics 47 (1), 43-68 (also presented in EACL 2021)
  Paper | Slides | Video | Arxiv

• Parsing Meaning Representations: is Easier Always Better?
  Zi Lin, Nianwen Xue
  ACL 2019 Workshop on Designing Meaning Representations (DMR)
  Paper | Slides

• Semantic Role Labeling for Learner Chinese: the Importance of Syntactic Parsing and L2-L1 Parallel Data
  Zi Lin, Yuguang Duan, Yuanyuan Zhao, Weiwei Sun, Xiaojun Wan
  EMNLP 2018
  Paper | Slides | Video | Arxiv | Data

Miscellany

• I learned western painting for nearly ten years and I still enjoy painting as an amateur, including sketching, acrylic painting and digital painting.