Language Models

Dissecting EEG-Language Models: Token Granularity, Model Size, and Cross-Site Generalization

We investigate how token granularity and model size affect EEG-language model performance in both in-distribution and cross-site scenarios, and find that token granularity is a critical, task-dependent scaling dimension for clinical EEG models, sometimes more important than model size.

Health system-scale language models are all-purpose prediction engines

We trained a large language model for medical language (NYUTron) and subsequently fine-tuned it across a wide range of clinical and operational predictive tasks, and found that it outperforms traditional models while being much easier to deploy.