Spring, 2025
Uyen "Rachel" Lai is looking into differences between the way AI algorithms that generate text, such as ChatGPT, express themselves compared to humans. The goal is to improve the usefulness of generative AI models in producing content.
“For example, my preliminary work suggests that generative AI models produce text that is more lexically diverse, though possibly less focused,” Lai says. “I want to make generative AI models that produce more “human sounding,” natural or realistic text.”
Lai, a fourth-year computer science honours student is working under the supervision of Paul Sheridan, assistant professor at the School of Mathematical and Computational Sciences at the University of Prince Edward Island. Even as an undergraduate, Lai has travelled as far as Tokyo to present a paper on her work with Sheridan Stable — Sheridan’s professional wrestling-inspired name for his “plucky” group of undergraduate researchers — which focuses on natural language processing and text analysis. The team is trying to understand how the two worlds of classic statistics and deep learning are connected.
“Deep learning inherits a lot of ideas from classical statistical methods. We’re looking at those connections and then mapping them out, which we hope will lead to insights about how to use all the statistics knowledge the world has accumulated over the years to do deep learning in more interpretable and efficient ways.”
Lai is setting the standard in that team of researchers.
“Rachel is an exceptional student who's on a good roll right now,” Sheridan says, “and there are lots more great students on the way who are hungry to make names for themselves.”
Lai uses ACENET resources to generate text using AI models, and she has also trained some of her fellow students on how to use them.
“We need a lot of text to compare lexical diversity in human-written and AI-generated text,” Lai says. “The compute clusters come in handy, helping us generate a massive amount of text. By using these resources, we can generate what we need in a few hours at most. Without them, it would take months or years to do our work.”
Sheridan says that as the program grows, the team will be generating vast volumes of text that will require hundreds of gigabytes which would be impractical to store on a regular computer.
“We need to use GPUs to generate text,” he says. “GPUs are in high-demand for deep learning applications.”
Lai lauds ACENET’s support services, which she says are also very responsive.
“If one person can’t help, they will find us someone else who has more expertise in the problem we are trying to solve,” she says.
