Li "Harry" Zhang  张力

About Me

I will be an assistant professor at Drexel University, starting in December 2024. Prospective students should email me and demonstrate an interest in my ongoing or related work.

I am a 5th- and final-year PhD student researcher focusing on Natural Language Processing and Artificial Intelligence, having the honor to be mentored by Prof. Chris Callison-Burch at the University of Pennsylvania. My thesis committee includes Prof. Dan Roth, Prof. Rada Mihalcea, Prof. Graham Neubig, Dr. Marianna Apidianaki, and Prof. Mark Yatskar. I got my BS from the University of Michigan in 2018, mentored by Prof. Rada Mihalcea and Prof. Dragomir Radev.



Drexel Universitydrexel logo
Assistant Professor;
Dec 2024 to Future

University of Pennsylvaniaupenn logo
Ph.D.; Aug 2019 to Aug 2024

Allen Institute for Artifical IntelligenceAI2
Research Intern;
April 2023 to Dec 2023

IBM ResearchIBM Research
Research Intern; May 2021 to Aug 2021,
April 2019 to June 2019

University of Michiganumich logo
B.S.E.; Aug 2015 to Dec 2018

Goldman SachsGoldman Sachs
Summer Analyst;
May 2017 to Aug 2017

Shenzhen Middle SchoolSMS logo
High School Diploma;
Sept 2012 to Jun 2015

Mentorship and Teaching

Mentored Students
Tianyi Zhang, University of Pennsylvania
Hainiu Xu, King's College London
Zhaoyi Hou, University of Pittsburg
Young-Min Cho, University of Pennsylvania
Manni Arora, Apple

I mentored each for more than 1 year. All graduated with a Master's degree from the University of Pennsylvania.

Teaching AssistantPenn

CIS 530: Computational Linguistics (Winter, Fall 2020)

Teaching AssistantUM-Penn
2016 - 2018

EECS 595: Natural Language Processing (Fall 2018) and EECS 280: Programming and Introductory Data Structures (Winter, Fall 2016)


I have reviewed more than 50 papers of and chaired for many NLP conferences and workshops.

Area Chair of ARR Feb 2024 (ACL 2024)
Reviewer of LREC-COLING 2024
Reviewer of EMNLP 2023
Program Chair of MASC-SLL 2023
Reviewer of ACL 2023
Program Chair of DaSH Workshop @ EMNLP 2022
Reviewer of COLING 2022
Reviewer of ARR Nov 2021, Mar 2022
Reviewer of LREC 2022
Program Chair of MASC-SLL 2021
Session Chair of AACL-IJCNLP 2020
Co-organizer of CLUNCH 2020
Reviewer of COLING 2020
Reviewer of Computer Speech and Language 2018


Primary Work: Structured Reasoning of Events using LLMs

Events and procedures play a major role in human language. Therefore, reasoning about them is crucial for AI and NLP. My work combines data-driven methods like large language models (LLM) and symbolic, structured representations of events to advance state-of-the-art on many downstream tasks, such as question answering, dialog, script generation, classical planning, etc. Roughly, I have looked into three types of methods:

1. (ongoing) Use LLMs to predict a structured, fully symbolic representation of events (e.g., in PDDL or Python), to be executed by symbolic solvers (e.g., planners or interpreters) for a more precise and deterministic reasoning process.

[28] PROC2PDDL: Open-Domain Planning Representations from Texts; Tianyi Zhang*Equal contribution^Mentored student, Li Zhang*Equal contribution, Zhaoyi Hou^Mentored student, Ziyu Wang^Mentored student, Yuling Gu, Peter Clark, Chris Callison-Burch and Niket Tandon; in preprint.Paper BibTeX Repo

2. Use LLMs to predict a structured, semi-symbolic representation of events (specifically, entities), which helps their decision making and reasoning via in-context learning.

[23] OpenPI2.0: An Improved Dataset for Entity Tracking in Texts; Li Zhang, Hainiu Xu^Mentored student, Abhinav Kommula, Chris Callison-Burch and Niket Tandon; in EACL 2024.Paper BibTeX Repo

[19] Causal Reasoning of Entities and Events in Procedural Texts; Li Zhang*Equal contribution, Hainiu Xu*Equal contribution^Mentored student, Yue Yang, Shuyan Zhou, Weiqiu You, Manni Arora and Chris Callison-Burch; in Findings of EACL 2023.Paper BibTeX Repo

3. Finetune LLMs with language-based data (specifically, event relations) to improve performance on various downstream tasks.

[6] Reasoning about Goals, Steps, and Temporal Ordering with WikiHow; Li Zhang*Equal contribution, Qing Lyu*Equal contribution and Chris Callison-Burch; in EMNLP 2020.Paper BibTeX Repo

[15] Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models; ... Li Zhang*Equal contribution, Qing Lyu*Equal contribution and Chris Callison-Burch; in TMLR.Paper

[10] Show Me More Details: Discovering Hierarchies of Procedures from Semi-structured Web Data; Shuyan Zhou*Equal contribution, Li Zhang*Equal contribution, Yue Yang, Qing Lyu, Pengcheng Yin, Chris Callison-Burch and Graham Neubig; in ACL 2022.Paper BibTeX Demo Repo

[8] Goal-Oriented Script Construction; Qing Lyu*Equal contribution, Li Zhang*Equal contribution and Chris Callison-Burch; in INLG 2021.Paper BibTeX Repo

[7] Intent Detection with WikiHow; Li Zhang, Qing Lyu, Chris Callison-Burch; in AACL-IJCNLP 2020.Paper BibTeX Repo

[9] Visual Goal-Step Inference using wikiHow; Yue Yang, Artemis Panagopoulou, Qing Lyu, Li Zhang, Mark Yatskar and Chris Callison-Burch; In EMNLP 2021.Paper BibTeX



I am a drummer, producer and video content creator. I run a video channel with over 50,000 subscribers on Bilibili and YouTube, primarily making cover songs from video game and anime soundtracks, in a variety of styles ranging from metal to jazz. I am proudly sponsored by Vater, Tama, Mackie, Alesis, NUX, Xvive and have collaborated with manufacturers of major video games such as Genshin Impact and Azur Lane. I also engage in research of AI music generation, having published a paper on automatic drum composition in an AAAI 2023 workshop.

[18] Language Models are Drummers: Drum Composition with Natural Language Pre-Training; Li Zhang and Chris Callison-Burch; in AAAI 2023 Workshop on Creative AI Across Modalities.Paper Repo BibTeX

My two albums A Doll's Lament (reimagined NieR soundtracks) and Dazzling Tales (reimagined Genshin Impact soundtracks) are available for listening on all major streaming platforms.

A Doll's Lament   Dazzling Tales