As LLMs become central to interactive applications, ranging from tutoring to mental health, the ability to express personality in culturally appropriate ways is increasingly important. While recent works have explored personality evaluation of LLMs, they largely overlook the interplay between culture and personality. To address this, we introduce , the first large-scale benchmark with human validation for evaluating LLMs’ personality expression in culturally grounded, behaviorally rich contexts. Our dataset spans 3,000 scenario-based questions across six diverse countries, designed to elicit personality through everyday scenarios rooted in local values. We evaluate how closely three models’ personality distributions align to real human populations through two evaluation settings: multiple-choice and open-ended response formats. Our results show– improves alignment with country-specific human personality distributions (over a 20% reduction in Wasserstein distance across models and countries) and elicits more expressive, culturally coherent outputs compared to existing benchmarks. surfaces meaningful modulate trait outputs in response to culturally grounded prompts, offering new directions for aligning LLMs to global norms of behavior. By bridging personality expression and cultural nuance, we envision that will pave the way for more socially intelligent and globally adaptive LLMs. Datasets and code are available at: https://github.com/limenlp/CulturalPersonas.

One important aspect of language is how speakers generate utterances and texts to convey their intended meanings. In this paper, we bring various aspects of the Construction Grammar (CxG) and the Systemic Functional Grammar (SFG) theories in a deep learning computational framework to model empathic language. Our corpus consists of 440 essays written by premed students as narrated simulated patient–doctor interactions. We start with baseline classifiers (state-of-the-art recurrent neural networks and transformer models). Then, we enrich these models with a set of linguistic constructions proving the importance of this novel approach to the task of empathy classification for this dataset. Our results indicate the potential of such constructions to contribute to the overall empathy profile of first-person narrative essays.

Empathy is a vital component of health care and plays a key role in the training of future doctors. Paying attention to medical students’ self-reflective stories of their interactions with patients can encourage empathy and the formation of professional identities that embody desirable values such as integrity and respect. We present a computational approach and linguistic analysis of empathic language in a large corpus of 440 essays written by pre-med students as narrated simulated patient – doctor interactions. We analyze the discourse of three kinds of empathy: cognitive, affective, and prosocial as highlighted by expert annotators. We also present various experiments with state-of-the-art recurrent neural networks and transformer models for classifying these forms of empathy. To further improve over these results, we develop a novel system architecture that makes use of frame semantics to enrich our state-of-the-art models. We show that this novel framework leads to significant improvement on the empathy classification task for this dataset.