Weiyan Shi is an incoming assistant professor at Northeastern University starting in 2024. She will spend 2023-2024 as a postdoc at Stanford NLP. Her research interests are in Natural Language Processing (NLP), especially in social influence dialogue systems such as persuasion, negotiation, and recommendation. She has also worked on privacy-preserving NLP applications. She is recognized as a Rising Star in Machine Learning by the University of Maryland. Her work on personalized persuasive dialogue systems was nominated for ACL 2019 best paper. She was also a core team member behind a Science publication on the first negotiation AI agent, Cicero, that achieves a human level in the game of Diplomacy. This work has been featured in The New York Times, The Washington Post, MIT Technology Review, Forbes, and other major media outlets.

Honors & Awards

  • Best Paper Nomination, ACL
  • Department Citation, UC, Berkeley
  • Department Commencement Speaker, UC, Berkeley
  • Rising Star in Machine Learning, University of Maryland

Stanford Advisors

Current Research and Scholarly Interests

My research interests are in Natural Language Processing, especially intelligent interactive systems and the following directions:

* Interactive systems specialized in social influence for social good (e.g., persuasive dialogues)
* Privacy-preserving NLP models
* Task-oriented and open-domain dialogue systems
* Intelligible dialogue generation
* Learning through interaction

My research vision is to build a natural interface between human intelligence and machine intelligence via natural conversations, so that all members of society can interact with AI models seamlessly regardless of their backgrounds.

All Publications

  • Human-level play in the game of Diplomacy by combining language models with strategic reasoning. Science (New York, N.Y.) Meta Fundamental AI Research Diplomacy Team (FAIR), Bakhtin, A., Brown, N., Dinan, E., Farina, G., Flaherty, C., Fried, D., Goff, A., Gray, J., Hu, H., Jacob, A. P., Komeili, M., Konath, K., Kwon, M., Lerer, A., Lewis, M., Miller, A. H., Mitts, S., Renduchintala, A., Roller, S., Rowe, D., Shi, W., Spisak, J., Wei, A., Wu, D., Zhang, H., Zijlstra, M. 2022: eade9097


    Despite much progress in training AI systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge. We introduce Cicero, the first AI agent to achieve human-level performance in Diplomacy, a strategy game involving both cooperation and competition that emphasizes natural language negotiation and tactical coordination between seven players. Cicero integrates a language model with planning and reinforcement learning algorithms by inferring players' beliefs and intentions from its conversations and generating dialogue in pursuit of its plans. Across 40 games of an anonymous online Diplomacy league, Cicero achieved more than double the average score of the human players and ranked in the top 10% of participants who played more than one game.

    View details for DOI 10.1126/science.ade9097

    View details for PubMedID 36413172