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 28/29, 2023 (TBD)
  • 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 (TBD)

  8:55 -   9:00Opening remarks
  9:00 -   9:45Invited Talk #1
  9:45 -   10:15Oral Presentation #1
  10:15 -   10:45Break / Meet-and-greet
  10:45 -   11:30Invited Talk #2
  11:30 -   12:15Invited Talk #3
  12:15 -   13:30Lunch / Poster Session / Spotlight
  13:30 -   14:15Invited Talk #4
  14:15 -   14:45Oral Presentation #2
  14:45 -   15:30Invited Talk #5
  15:30 -   16:00Break / Meet-and-greet
  16:00 -   17:00Panel discussion (all invited speakers)
  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.

Ian Apperly

Ian Apperly is an experimental psychologist, and his main research interest is in “mindreading” – the ability to take other people’s perspectives for communication, co-operation, competition or deception. He is the author of over 80 journal articles, and the 2010 book, entitled “Mindreaders: The cognitive basis of theory of mind”.

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.

Joyce Chai

Joyce Y. Chai is a Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan. Prior to joining UM in 2019, she was a Professor of Computer Science and Engineering at Michigan State University. She also spent a couple years at the IBM T. J. Watson Research Center as a research staff member before joining MSU in 2003. Her research interests include natural language processing, situated dialogue, human-robot communication, and artificial intelligence. Her recent work explores the intersection of language, vision, and robotics, particularly focusing on grounded language processing to facilitate situated communication with robots and other artificial agents. She has served on the executive board of North America Chapter of Association for Computational Linguistics (NAACL), as a Program Co-chair for multiple conferences - most recently the 2020 Annual Meeting of Association for Computational Linguistics (ACL), and as an associate editor for several journals including Computational Linguistics, Journal of Artificial Intelligence Research (JAIR), and ACM Transaction on Interactive Intelligent Systems (TiiS). She is a recipient of the National Science Foundation Career Award (2004), the William Beal Distinguished Scholar Award from MSU (2018), and a number of paper awards including the Best Long Paper Award from ACL (2010) and an Outstanding Paper Award from EMNLP (2021). She holds a Ph.D. in Computer Science from Duke University.


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)
  • Shane Steinert-Threlkeld (University of Washington)
  • Sean Dae Houlihan (Dartmouth)
  • Erin Grant (Berkeley)
  • Ted Sumers (Princeton)
  • Ece Takmaz (UVA)
  • Tan Zhi-Xuan (MIT)