Papers by Kaleen Shrestha

3 papers
Using Linguistic Entrainment to Evaluate Large Language Models for Use in Cognitive Behavioral Therapy (2025.findings-naacl)

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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.
Evaluating Behavioral Alignment in Conflict Dialogue: A Multi-Dimensional Comparison of LLM Agents and Humans (2025.emnlp-main)

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Challenge: Large Language Models (LLMs) are increasingly used in socially complex, interaction-driven tasks, yet their ability to mirror human behavior in emotionally and strategically complex contexts remains underexplored.
Approach: They examine alignment of personality-prompted Large Language Models in conflict dialogues that incorporate negotiation by simulating a five-factor personality profile.
Outcome: The proposed model achieves the closest alignment with humans in linguistic style and emotional dynamics while Claude-3.7-Sonnet best reflects strategic behavior.

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