Large language models like ChatGPT have shown extraordinary abilities for writing. While impressive at first glance, large language models aren't perfect and often make mistakes humans would not make. The main architecture behind ChatGPT mostly doesn't differ from early neural networks and, as a consequence, carries some of the same limitations. My work revolves around the use of neural networks like ChatGPT mixed with symbolic methods from early AI and how these two families of methods can combine to create more robust AI. I talk about some of the neurosymbolic methods I used for applications in story generation and understanding -- with the goal of eventually creating AI that can play Dungeons & Dragons. I also discuss pain points that I found for improving accessible communication and show how large language models can supplement such communication.

Dr. Martin will teach a special topics class at UMBC in Fall 2024 on Interactive Fiction and Text Generation (CMSC 491/691) based on a similar course she developed and co-taught at the University of Pennsylvania in 2022.

View the video on YouTube