Large Language Models are transforming natural language processing tasks in multiple domains in many ways. Despite their capabilities, their real-world adoption is often limited by issues like the lack of transparency, inadequate understanding of domain protocols, and subpar precision.
Manas Gaur, Ed Raff, and Ali Mohammadi were part of the team that organized and presented a half-day tutorial at the 2025 AAAI Conference last month covering the concept of Neurosymbolic AI and how it can be applied to LLMs to help solve key challenges in NLP tasks like explainability, grounding, and instructability.
You can see their slides and other material here.
UMBC Center for AI