Welcome to the

Workshop on Theory-of-Mind

at ICML 2023

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ToM @ ICML 2023


Theory of Mind (ToM) is the ability to reason about the minds of other agents. The main theme of our workshop is the computational modeling of ToM, with a special focus on the role of natural language in such modeling. Specifically, ToM 2023 pays attention to cognitive foundations and theories of ToM, the acquisition and relationship between language and ToM, leveraging ToM to improve and explain NLP and ML models, and using ToM for positive social impact. This workshop intends to promote the community of researchers that are interested in improving the ability of intelligent agents to reason about others' mental states. Our proposed program provides a space to discuss pathways for understanding and applying ToM in psycholinguistics, pragmatics, human value alignment, social good, model explainability, and many other areas of NLP. ToM 2023 will be a full-day hybrid in-person/virtual workshop with several keynote speeches, and oral/poster/spotlight presentations, followed by a breakout discussion, panel discussion, and best paper award announcement. We also intend to host a mentoring program to broaden participation from a diverse set of researchers.

The ToM Workshop will be co-located with ICML 2023!

If you have any questions, feel free to contact us.


Call for papers

We welcome submissions of full papers as well as work-in-progress and accept submissions of work recently published or currently under review.

In general, we encourage three types of papers:

  • Empirical paper: Submissions should focus on presenting original research, case studies or novel implementations in the fields of machine learning, artificial intelligence, natural language processing, and related areas.
  • Position paper: Authors are encouraged to submit papers that discuss critical and thought-provoking topics within the scientific community.
  • Thought pieces: Contributions in this category should provide insights, thought experiments, or discussions pertaining to theoretical or philosophical questions in machine learning and related disciplines. We welcome the papers discussing the relationships between theory-of-mind and language acquisition, large language models, agency, subjectivity, embodiment, AI ethics and safety, social intelligence, artificial intelligence, etc. We also welcome contributions that address related questions or offer new perspectives on existing conversations.
Potential topics include:
  • Leveraging ToM for Machine Learning Applications (e.g., NLP, Robotics, CV)
  • Cognitive Science Perspectives of ToM
  • ToM for HCI / Human-AI collaboration
  • Surveys or replication of existing work
  • Social Impacts of ToM
Important Dates
  • Submission deadline: May 26, 2023
  • Notification of acceptance: June 16, 2023
  • Camera-ready papers due: June 23, 2023
  • Workshop dates:📌 July 28, 2023
  • All deadlines are AoE time.
Submission Guidelines

The ToM 2023 workshop will use OpenReview as the review platform.

Accepted papers will be presented as posters, and a subset of them will be selected for oral presentation. We plan to organize the ToM workshop at ICML 2023 in a hybrid format. For virtual workshop attendees, we plan to use Zoom for the talks/panel and Gather for posters/socializing. To support the hybrid format, we will hold parallel meet-and-greet sessions online and in-person.

The paper template and style files can be found at here (adapted based on ICML template). The length of paper can be as short as 2 pages or as long as 8 pages (excluding references and appendix). Submissions must follow the template and style and should be properly anonymized.

Dual Submission Policy

We welcome papers that have never been submitted, are currently under review, or recently published. Accepted papers will be published on the workshop homepage, but will not be part of the official proceedings and are to be considered non-archival.


Workshop schedule

🚪Room 317 A

  8:55 -   9:00Opening remarks
  9:00 -   9:45Invited Talk (Maarten Sap)
  9:45 -   10:15Oral Presentation #1
  10:15 -   10:45Break / Meet-and-greet
  10:45 -   11:30Invited Talk (Noga Zaslavsky)
  11:30 -   12:15Invited Talk (Mark Ho)
  12:15 -   13:30Lunch / Poster Session / Spotlight
  13:30 -   14:15Invited Talk (Jan-Philipp Fränken)
  14:15 -   14:45Oral Presentation #2
  14:45 -   15:30Invited Talk (Kevin R. McKee)
  15:30 -   16:00Break / Meet-and-greet
  16:00 -   17:00Panel discussion (Kevin, Jesse, Bodhisattwa) [sli.do link]
  17:00 -   17:10Closing remarks / Best paper award


Invited Speakers

Maarten Sap

Maarten Sap is an assistant professor in Carnegie Mellon University's Language Technologies Department (CMU LTI), and a part-time research scientist at the Allen Institute for AI. His research focuses on making NLP systems socially intelligent, and understanding social inequality and bias in language. He has presented his work in top-tier NLP and AI conferences, receiving a best short paper nomination at ACL 2019 and a best paper award at the WeCNLP 2020 summit. His research has been covered in the New York Times, Forbes, Fortune, and Vox. Additionally, he and his team won the inaugural 2017 Amazon Alexa Prize, a social chatbot competition. Before joining CMU, he was a postdoc/young investigator at the Allen Institute for AI (AI2) on project MOSAIC. He received his PhD from the University of Washington's Paul G. Allen School of Computer Science & Engineering where he was advised by Yejin Choi and Noah Smith. In the past, he has interned at the Allen Institute for AI working on social commonsense reasoning, and at Microsoft Research working on deep learning models for understanding human cognition.

Jan-Philipp Franken

Jan-Philipp Fränken is a post-doctoral researcher at Stanford University. He currently works on reasoning in language models and uses language models to generate synthetic datasets (e.g., evaluations). More generally, Philipp is interested in developing self-improving agents with social reasoning capabilities. Philipp completed his PhD at the University of Edinburgh working on concept learning and social learning in multi-player games.

Noga Zaslavsky

Noga Zaslavsky is a K. Lisa Yang Integrative Computational Neuroscience (ICoN) Postdoctoral Fellow at MIT, collaborating with multiple labs including the Computational Psycholinguistics Lab, TEvLab, MetaConscious Group, and the Computational Cognitive Science Group. Noga's research aims to understand language, learning, and reasoning from first principles, building on ideas and methods from machine learning and information theory. Noga is particularly interested in finding computational principles that explain how we use language to represent the environment; how this representation can be learned in humans and in artificial neural networks; how it interacts with other cognitive functions, such as perception, reasoning, and decision making; and how it evolves over time and adapts to changing environments and social needs. Noga believes that such principles could advance our understanding of human cognition and guide the development of artificial agents that can communicate and collaborate with humans.

Mark Ho

Mark Ho is currently a Faculty Fellow in the NYU Center for Data Science. Previously, Mark was a post-doctoral researcher working with Tom Griffiths and Jonathan Cohen at Princeton University in the Computer Science and Psychology departments. His research focuses on developing computational theories of human planning. Prior to this, he was affiliated with the Learn and Verify Group at UC Berkeley's Department of Electrical Engineering and Computer Sciences. Mark obtained his Ph.D. in Cognitive Science from Brown University, where he worked with Joseph Austerweil and Fiery Cushman on teaching and social learning. He also holds an M.S. in Computer Science, which he earned while working with Michael Littman on interactive machine learning. Mark completed his undergraduate studies at Princeton, where he majored in Philosophy and minored in Computer Science, with Gil Harman advising his senior thesis. In Fall 2023, Mark will be starting as a tenure-track Assistant Professor in the Department of Computer Science at Stevens Institute of Technology.

Kevin McKee

Kevin R. McKee is a research scientist at DeepMind. Artificial intelligence and machine learning algorithms play an increasingly large role in our day-to-day lives. This expansion poses important questions. Do these AI systems affect the social dynamics in our lives? How do they change the way that we connect and relate to each other? Can we design algorithms and systems that better support cooperation and positive social interaction? Kenvin's research broadly aims to explore the relationships developing between human and artificial intelligence.

Invited Panelists

Jesse Thomason

Jesse Thomason is an Assistant Professor at the University of Southern California Viterbi School of Engineering in the Computer Science department. Recently, he was a visiting academic at Amazon Alexa AI and a postdoctoral researcher at the University of Washington. His research focuses on language grounding and natural language processing applications for robotics (RoboNLP), including how dialog with humans can facilitate both robot task execution and learning.

Bodhisattwa Majumder

Bodhisattwa Majumder is a Research Scientist at Allen Institute of AI. He works on the Aristo project. His research focuses on Interactive Agents, Machine Reasoning, Explanations, Personalization, and Social Science. He received my Ph.D. in Computer Science from UC San Diego, advised by Julian McAuley. He is a recipient of the UCSD CSE Doctoral Award for Excellence in Research (2022), Adobe Research Fellowship (2022), Qualcomm Innovation Fellowship (2020), and led UCSD (Team Bernard) in the finals of Amazon Alexa Prize, 2019.


Workshop Organizers

Hao Zhu

Hao Zhu

Ph.D. student at Carnegie Mellon University
Alane Suhr

Alane Suhr

Young Investigator at Allen Institute for AI
Chenghao Yang

Chenghao Yang

Ph.D. student at University of Chicago
Jennifer Hu

Jennifer Hu

Ph.D. student at Massachusetts Institute of Technology
Pei Zhou

Pei Zhou

Ph.D. student at University of Southern California
Saujas Vaduguru

Saujas Vaduguru

Ph.D. student at Carnegie Mellon University
Xuhui Zhou

Xuhui Zhou

Ph.D. student at Carnegie Mellon University
Hyunwoo Kim

Hyunwoo Kim

Ph.D. candidate at Seoul National University

Advisory Board

Yonatan Bisk

Yonatan Bisk

Carnegie Mellon University
Graham Neubig

Graham Neubig

Carnegie Mellon University
Daniel Fried

Daniel Fried

Carnegie Mellon University

Program Committee

  • Yuwei "Emily" Bao (University of Michigan)
  • Tianmin Shu (MIT)
  • Melanie Sclar (University of Washington)
  • Luyao Yuan (Meta)
  • Yewen Pu (Autodesk Research)
  • Robert Hawkins (Princeton University → UW Madison)
  • Natalie Shapira (Bar-Ilan University)
  • Cathy Jiao (CMU)
  • Minglu Zhao (University of California, Los Angeles)
  • Theodore Sumers (Princeton University)
  • Yuwei Sun (University of Tokyo)
  • Kartik Chandra (Massachusetts Institute of Technology)
  • Luca Bischetti (Istituto Universitario di Studi Superiori)
  • Lion Schulz (Max Planck Institute for Biological Cybernetics)
  • Siddhant Bhambri (Arizona State University)
  • Suhong Moon (University of California Berkeley)
  • Ziluo Ding (Peking University)
  • Renze Lou (Pennsylvania State University)
  • Ini Oguntola (Carnegie Mellon University)
  • Herbie Bradley (University of Cambridge)
  • Kai Zhang (Ohio State University, Columbus)
  • Shuwen Qiu (University of California, Los Angeles)
  • Guillaume Dumas (Université de Montréal)
  • Anfisa Chuganskaya (Lomonosov Moscow State University)
  • Mudit Verma (Arizona State University)
  • Alfredo Garcia (Texas A&M University - College Station)
  • Erin Grant (University College London)
  • Mine Caliskan (Eberhard-Karls-Universität Tübingen)
  • Laura Ruis (University College London, University of London)
  • Ece Takmaz (University of Amsterdam)
  • Cameron Jones (University of California, San Diego)
  • Minhae Kwon (Soongsil University)
  • Tan Zhi-Xuan (Massachusetts Institute of Technology)
  • Chaoqi Wang (University of Chicago)
  • Alexey Kovalev (AIRI)
  • Peter Dayan (Max-Planck Institute)
  • Dongsu Lee (soongsil university)
  • Shane Steinert-Threlkeld (University of Washington, Seattle)
  • Eliza Kosoy (University of California Berkeley)
  • Cathy Jiao (Carnegie Mellon University)
  • Soham Dinesh Tiwari (Carnegie Mellon University)