Can One Model Rule Them All? Tailoring Large Language Models to Specialized Domains, Specific Populations, and Unique Individuals

LUNCH:
Lunch will be served at 11:45 AM.

ABSTRACT:
The last decade saw a dramatic shift in how NLP systems are developed and deployed: from bespoke models trained for specific tasks to a single Large Language Model (LLM) capable of solving a variety of tasks via in-context learning (ICL). In this talk, I will discuss the problem of adapting LLMs to out-of-distribution tasks and datasets.

SPEAKER BIO:
Silvio Amir is an assistant professor at the Khoury College of Computer Science at Northeastern University. He completed his PhD at the University of Lisbon and was a postdoc at Johns Hopkins University before joining Northeastern. Silvio Amir works on Natural Language Processing and Machine Learning methods to analyze personal and user generated text, such as social media and clinical notes from Electronic Health Records. He is primarily interested in tasks involving subjective, personalized or user-level inferences (e.g., opinion mining and digital phenotyping). In particular, his work aims to improve the reliability, interpretability and fairness of predictive models and analytics derived from these data.