Ziqi Wang

I am a Ph.D. student at the University of Illinois Urbana-Champaign, advised by Prof. Heng Ji, working on natural language processing.

I spent two summers at Google as a Software Engineering Intern with Dr. Crick Wu and Dr. Le Hou. Prior to my Ph.D. study, I obtained a Bachelor's Degree in Computer Science at Tsinghua University, where I was fortunate to work with Prof. Zhiyuan Liu, Prof. Xiaolin Hu, Prof. Minlie Huang, and Prof. Xiang Ren at the University of Southern California.

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My research goal is to use AI to automate research, therefore helping humans understand the universe. At the current stage, I am interested in (1) enabling AI to continually learn novel knowledge to self-improve in a data-sufficient or data-efficient regime, and (2) make AI proactively observe, interact, and then acquire novel knowledge from the environment (physical world, internet, humans, other agents) signals.

Selected Publications (See full list on

Google Scholar


* denotes equal contribution

Enabling Language Models to Implicitly Learn Self-Improvement From Data
Ziqi Wang, Le Hou, Tianjian Lu, Yuexin Wu, Yunxuan Li, Hongkun Yu, Heng Ji
ICLR, 2024  
Paper / Slides / Twitter

A framework that learns self-improvement from preference data that aligns better human intention.

Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation
Ziqi Wang, Yuexin Wu, Frederick Liu, Daogao Liu, Le Hou, Hongkun Yu, Jing Li, Heng Ji
ICLR, 2023  
Paper / Slides / Talk Recording / Poster

We propose a simple data augmentation method that benefit knowledge distillation in a data-efficient regime.

Recognizing Object by Components With Human Prior Knowledge Enhances Adversarial Robustness of Deep Neural Networks
Xiao Li*, Ziqi Wang*, Bo Zhang, Fuchun Sun, Xiaolin Hu
TPAMI, 2023  
Paper / Code

We reveal that human prior knowledge can serve as an important component to improve vision robustness to attacks.

CLEVE: Contrastive Pre-training for Event Extraction
Ziqi Wang*, Xiaozhi Wang*, Xu Han, Yankai Lin, Lei Hou, Zhiyuan Liu, Peng Li, Juanzi Li, Jie Zhou
ACL, 2021  
Paper / Slides / Talk Recording / Code

We show that event-oriented contrastive pre-training can enhance event representations and understanding.

Learning from Explanations with Neural Execution Tree
Ziqi Wang*, Yujia Qin*, Wenxuan Zhou, Jun Yan, Qinyuan Ye, Leonardo Neves, Zhiyuan Liu, Xiang Ren
ICLR, 2020  
Paper / Project Website / Slides / Talk Recording / Code

We propose to use human explanations to build neuro-symbolic networks. The networks can then label pseudo labels to improve semi-supervised training.

NERO: A Neural Rule Grounding Framework for Label-Efficient Relation Extraction
Wenxuan Zhou, Hongtao Lin, Yuchen Lin, Ziqi Wang, Junyi Du, Leonardo Neves, Xiang Ren
WWW, 2020   (Best Paper Runner-Up, 2/1500+)
Paper / Code

We propose to use neural rules to ground data-efficient learning in relation extraction.

HMEAE: Hierarchical Modular Event Argument Extraction
Xiaozhi Wang*, Ziqi Wang*, Xu Han, Zhiyuan Liu, Juanzi Li, Peng Li, Maosong Sun, Jie Zhou, Xiang Ren
EMNLP-IJCNLP, 2019   (Oral Presentation)
Paper / Slides / Talk Recording / Code

We use human prior knowledge to make models aware of the semantic relations between labels and thus perform better in the event argument extraction task.


University of Illinois Urbana-Champaign
Ph.D. in Computer Science, 2021-

Advisor: Prof. Heng Ji
Tsinghua University
B.E. in Computer Science, 2016-2021

Advisor: Prof. Zhiyuan Liu, Prof. Xiaolin Hu, and Prof. Minlie Huang


Reviewer: ACL/ARR 2024 Feb, NAACL/ARR 2023 Dec, ICML 2024, ICLR 2024, NeurIPS 2023, ACL 2023 Demo, Pattern Recognition


Enabling Language Models to Implicitly Learn Self-Improvement From Data. Objective, Inc., 2023. [Slides]


In addition to my research work, I find pleasure in coding. I develop websites, applications, and other software programs. Although some of my previous projects have been lost over time, I have managed to archive several of them.
COVID-19 Claim Radar: A Structured Claim Extraction and Tracking System

Built with Go and Vue.js.

Demo / Code

A web interface that shows claims about COVID-19 crawled from the Internet. The system details can be found in this paper, which is accepted at ACL 2022 (System Demonstration), and the implementation.

SmartBook: AI-Assisted Situation Report Generation

Built with Vue.js.

Demo / Code

A web interface that shows situation reports generated by AI. The system details can be found in this paper and the implementation.


I am interested in astronomy in my spare time. Repairment and DIY are also my favorite since they help me understand the bottom mechanism. I am a big fan of John Carmack and Elon Musk.

The website is adapted from Jon Barron. Last update: Feb, 2024.