Humans are able to predict each other’s actions by reasoning about the others’ underlying goals, preferences, and motives, such as greed and risk-aversion. Game theory provides a framework for studying human behaviors through incentivized games that simulate social situations. We utilized two validated games from the cognitive science literature—the Social Prediction Game (SPG) and the Inspection Game (IG)—to systematically study how well several recent open- and closed-source LLMs predict player actions and whether they can leverage and generalize the players’ motives learned from the iterated games. Our results indicate that state-of-the-art LLMs can achieve accuracy close to human levels in predicting players’ actions with underlying human motives in SPGs. However, unlike humans, who rely on reasoning about players’ motives to inform their predictions, LLMs failed to recognize statistical patterns in players’ actions. As a result, LLM prediction accuracy did not improve over multiple rounds. Our results in the IG further demonstrate that, unlike humans, LLMs were unable to recognize a player’s underlying motives and to generalize their understanding of the same player to a new context. This suggests that LLMs may lack reasoning capabilities. Our findings offer insights into differences in human and LLM reasoning mechanisms, suggesting that further research into human-AI alignment is needed before utilizing LLMs for human behavior modeling and simulation in this and related contexts.

Entrainment, the responsive communication between interacting individuals, is a crucial process in building a strong relationship between a mental health therapist and their client, leading to positive therapeutic outcomes. However, so far entrainment has not been investigated as a measure of efficacy of large language models (LLMs) delivering mental health therapy. In this work, we evaluate the linguistic entrainment of an LLM (ChatGPT 3.5-turbo) in a mental health dialog setting. We first validate computational measures of linguistic entrainment with two measures of the quality of client self-disclosures: intimacy and engagement (p < 0.05). We then compare the linguistic entrainment of the LLM to trained therapists and non-expert online peer supporters in a cognitive behavioral therapy (CBT) setting. We show that the LLM is outperformed by humans with respect to linguistic entrainment (p < 0.001). These results support the need to be cautious in using LLMs out-of-the-box for mental health applications.

Large Language Models (LLMs) are increasingly deployed in socially complex, interaction-driven tasks, yet their ability to mirror human behavior in emotionally and strategically complex contexts remains underexplored. This study assesses the behavioral alignment of personality-prompted LLMs in adversarial dispute resolution by simulating multi-turn conflict dialogues that incorporate negotiation. Each LLM is guided by a matched Five-Factor personality profile to control for individual variation and enhance realism. We evaluate alignment across three dimensions: linguistic style, emotional expression (e.g., anger dynamics), and strategic behavior. GPT-4.1 achieves the closest alignment with humans in linguistic style and emotional dynamics, while Claude-3.7-Sonnet best reflects strategic behavior. Nonetheless, substantial alignment gaps persist. Our findings establish a benchmark for alignment between LLMs and humans in socially complex interactions, underscoring both the promise and the limitations of personality conditioning in dialogue modeling.