Julia Soares Dollis

Fernanda Bufon Färber, Iago Alves Brito, Julia Soares Dollis et al.

While large language models (LLMs) show transformative potential in healthcare, their development remains focused on high-resource languages, creating a critical barrier for others as simple translation fails to capture unique clinical and cultural nuances, such as endemic diseases. To address this, we introduce MedPT, the first large-scale, real-world corpus for Brazilian Portuguese, comprising 384,095 authentic question-answer pairs from patient-doctor interactions. The dataset underwent a meticulous multi-stage curation protocol, using a hybrid quantitative-qualitative analysis to filter noise and contextually enrich thousands of ambiguous queries. We further augmented the corpus via LLM-driven annotation, classifying questions into seven semantic types to capture user intent. Our analysis reveals its thematic breadth (3,200 topics) and unique linguistic properties, like the natural asymmetry in patient-doctor communication. To validate its utility, we benchmark a medical specialty routing task: fine-tuning a 1.7B parameter model achieves an outstanding 94\% F1-score on a 20-class setup. Furthermore, our qualitative error analysis shows misclassifications are not random but reflect genuine clinical ambiguities (e.g., between comorbid conditions), proving the dataset's deep semantic richness. We publicly release MedPT to foster the development of more equitable, accurate, and culturally-aware medical technologies for the Portuguese-speaking world.

Iago Alves Brito, Julia Soares Dollis, Fernanda Bufon Färber et al.

The integration of large language models (LLMs) into virtual reality (VR) environments has opened new pathways for creating more immersive and interactive digital humans. By leveraging the generative capabilities of LLMs alongside multimodal outputs such as facial expressions and gestures, virtual agents can simulate human-like personalities and emotions, fostering richer and more engaging user experiences. This paper provides a comprehensive review of methods for enabling digital humans to adopt nuanced personality traits, exploring approaches such as zero-shot, few-shot, and fine-tuning. Additionally, it highlights the challenges of integrating LLM-driven personality traits into VR, including computational demands, latency issues, and the lack of standardized evaluation frameworks for multimodal interactions. By addressing these gaps, this work lays a foundation for advancing applications in education, therapy, and gaming, while fostering interdisciplinary collaboration to redefine human-computer interaction in VR.

Iago Alves Brito, Walcy Santos Rezende Rios, Julia Soares Dollis et al.

Current safety evaluations of large language models (LLMs) create a dangerous illusion of universality, aggregating "Identity Hate" into scalar scores that mask systemic vulnerabilities against specific populations. To expose this selective safety, we introduce MiJaBench, a bilingual (English and Portuguese) adversarial benchmark comprising 44,000 prompts across 16 minority groups. By generating 528,000 prompt-response pairs from 12 state-of-the-art LLMs, we curate MiJaBench-Align, revealing that safety alignment is not a generalized semantic capability but a demographic hierarchy: defense rates fluctuate by up to 33\% within the same model solely based on the target group. Crucially, we demonstrate that model scaling exacerbates these disparities, suggesting that current alignment techniques do not create principle of non-discrimination but reinforces memorized refusal boundaries only for specific groups, challenging the current scaling laws of security. We release all datasets and scripts to encourage research into granular demographic alignment at GitHub.