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Maarten Sap

I am an assistant professor at CMU's LTI department with a courtesy appointment in HCII, and a part-time research scientist and AI safety lead at the Allen Institute for AI (AI2). My research focuses on (1) measuring and improving AI systems' social and interactional intelligence, (2) assessing and combatting social inequality, safety risks, and socio-cultural biases in human- or AI-generated language, and (3) building narrative language technologies for prosocial outcomes. I was named a 2025 Packard Fellow and a recipient of the 2025 Okawa Research Award.

I received my PhD from the University of Washington where I was advised by Noah Smith and Yejin Choi.
[bio for talks]

Recent updates:

December 2025 🏅📃: Very excited to have our paper Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond) selected for a Best Paper Award at NeurIPS 2025 (Datasets and Benchmarks Track)!! Huge congrats to the first author Liwei Jiang!!!

November 2025 💎🚀: Honored to be a Spring 2025 recipient of the Amazon Research Award for our project on measuring AI agentic safety!

October 2025 🏅⭐: I’m super excited and grateful to announce that I'm part of the 2025 class of Packard Fellows. The Packard Foundation and this fellowship will allow me to explore exciting research directions towards culturally responsible and safe AI 🌍🌈

October 2025 🔍🧑‍🎓: Due to my lab being quite full already, I'm not taking looking for any new students in this upcoming PhD application cycle 😟.

October 2025 🇨🇦🎉: Excited to be attending COLM 2025 in Montreal this October! I'll be giving a talk at the Social Sim Workshop on Unlocking Social Intelligence in AI agents. I'm also thrilled that five papers I co-authored will be presented by my amazing collaborators at COLM: HAICOSYSTEM: An Ecosystem for Sandboxing Safety Risks in Human-AI Interactions (led by Xuhui Zhou et al.), ALFA: Aligning LLMs to Ask Good Questions: A Case Study in Clinical Reasoning (co-led by Jimin Mun et al.), PolyGuard: A Multilingual Safety Moderation Tool for 17 Languages, Fluid Language Model Benchmarking, and The Delta Learning Hypothesis: Preference Tuning on Weak Data can Yield Strong Gains.

August 2025 🌟: Incredibly honored to be one of 7 US recipients of the 2025 Okawa Research Grant from the Okawa Foundation!

August 2025 🧑‍🎓: Welcoming my first postdoc, Vasudha Varadarajan, to the lab!

[older news]

My research group:

Dan Chechelnitsky

CMU Portugal LTI PhD student
co-advised with Chrysoula Zerva

Joel Mire

LTI PhD student

Karina Halevy

LTI PhD student
co-advised with Mona Diab

Jimin Mun

LTI PhD student

Jocelyn Shen

MIT PhD student
co-advised with Cynthia Breazeal

Kynnedy Smith

HCII PhD student
co-advised with Motahhare Eslami

Vasudha Varadarajan

LTI Postdoc

Akhila Yerukola

LTI PhD student

Mingqian Zheng

LTI PhD student
co-advised with Carolyn Rosé

Xuhui Zhou

LTI PhD student

Overarching Research Themes

Themes extracted and images generated with the OpenAI API; there may be inconsistencies.

Ethics in AI Practices

My research group explores the complex terrain of ethics in artificial intelligence, focusing on responsible deployment and evaluation frameworks. A significant paper, [OpenAgentSafety: A Comprehensive Framework for Evaluating Real-World AI Agent Safety](https://arxiv.org/abs/2507.06134), identifies critical safety considerations for AI agents in real-world contexts. Additionally, our work on [PluriHarms: Benchmarking the Full Spectrum of Human Judgments on AI Harm](https://arxiv.org/abs/2601.08951) seeks to comprehensively measure diverse human perceptions around AI-related harms. We also investigate the importance of user perceptions through papers like [AI-LieDar: Examine the Trade-off Between Utility and Truthfulness in LLM Agents](https://aclanthology.org/2025.naacl-long.595/), which assesses trade-offs in AI behavior that impact trustworthiness and utility.

Understanding and Analyzing Narratives

My research group explores the intricacies of narratives and how they shape human communication and interpretation. One of our recent impactful studies is [Social Story Frames: Contextual Reasoning about Narrative Intent and Reception](https://arxiv.org/abs/2512.15925), which examines how people discern the intentions behind various narratives. We also delve into the effects of violent communication through our work on [Words Like Knives: Backstory-Personalized Modeling and Detection of Violent Communication](https://arxiv.org/abs/2505.21451), offering insights on backstory and context-driven interpretations. The empirical study [The Empirical Variability of Narrative Perceptions of Social Media Texts](https://aclanthology.org/2024.emnlp-main.1113/) further contributes to understanding how different audiences can variably perceive the same narrative.

Social Intelligence in AI Systems

My research group explores the development of AI systems that exhibit social intelligence and emotional understanding in interactions. Our paper [SOTOPIA: Interactive Evaluation for Social Intelligence in Language Agents](https://arxiv.org/abs/2310.11667) introduces a framework for measuring social capabilities in language agents, emphasizing the importance of social reasoning. Additionally, we investigate social reasoning capabilities in embodied interactions as evidenced in [SoMi-ToM: Evaluating Multi-Perspective Theory of Mind in Embodied Social Interactions](https://arxiv.org/abs/2506.23046). Finally, our examination of simulated social interactions, highlighted in [Is This the Real Life? Is This Just Fantasy? The Misleading Success of Simulating Social Interactions With LLMs](http://arxiv.org/abs/2403.05020), pushes the boundaries of how AI can understand and mimic real-world social contexts.

Innovative Human-AI Collaboration

My research group explores novel methodologies for enhancing collaborative efforts between humans and AI systems. For instance, we have developed [ALFA: Aligning LLMs to Ask Good Questions: A Case Study in Clinical Reasoning](https://arxiv.org/abs/2502.14860), which focuses on improving AI's ability to engage users through effective questioning techniques in medical settings. Our work also extends to addressing language model safety through [PolyGuard: A Multilingual Safety Moderation Tool for 17 Languages](https://arxiv.org/abs/2504.04377), which ensures healthier interactions across diverse linguistic contexts. Moreover, the study on [1-2-3 Check: Enhancing Contextual Privacy in LLM via Multi-Agent Reasoning](https://arxiv.org/abs/2508.07667) highlights innovative strategies for maintaining user privacy while collaborating with AI.

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