Papers by Maja Mataric
Using Linguistic Entrainment to Evaluate Large Language Models for Use in Cognitive Behavioral Therapy (2025.findings-naacl)
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Mina Kian, Kaleen Shrestha, Katrin Fischer, Xiaoyuan Zhu, Jonathan Ong, Aryan Trehan, Jessica Wang, Gloria Chang, Séb Arnold, Maja Mataric
| Challenge: | Entrainment is a communication process that builds a strong relationship between a mental health therapist and their client. |
| Approach: | They evaluate the linguistic entrainment of an LLM in a mental health dialog setting and compare it to trained therapists and non-expert online peer supporters. |
| Outcome: | The proposed model outperforms humans in a cognitive behavioral therapy setting. |
Can Large Language Models Infer Human Actions and Motives? Evaluation in Social Prediction and Inspection Games (2026.findings-acl)
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| Challenge: | Game theory provides a framework for studying human behaviors through incentivized games that simulate social situations. |
| Approach: | They used two validated games from the cognitive science literature to study how well several recent open- and closed-source LLMs predict player actions with underlying human motives. |
| Outcome: | The results show that state-of-the-art LLMs can achieve accuracy close to human levels in predicting players’ actions with underlying human motives in SPGs, but failed to recognize statistical patterns in players’ action. |
Sleepless Nights, Sugary Days: Creating Synthetic Users with Health Conditions for Realistic Coaching Agent Interactions (2025.findings-acl)
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Taedong Yun, Eric Yang, Mustafa Safdari, Jong Ha Lee, Vaishnavi Vinod Kumar, S. Sara Mahdavi, Jonathan Amar, Derek Peyton, Reut Aharony, Andreas Michaelides PhD, Logan Douglas Schneider, Isaac Galatzer-Levy, Yugang Jia, John Canny, Arthur Gretton, Maja Mataric
| Challenge: | Structured data is generated grounded in health and lifestyle factors and full profiles of synthetic users are developed conditioned on the structured data. |
| Approach: | They propose an end-to-end framework for generating synthetic users for evaluating interactive agents designed to encourage positive behavior changes, such as in health and lifestyle coaching. |
| Outcome: | The proposed framework is validated in the domains of sleep and diabetes coaching using two independently-developed agents for sleep and diabetic coaching as case studies. |