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
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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
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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
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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
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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.