Abstract:

Large Language Models (LLMs) are often seen as powerful yet static entities, their knowledge frozen after training, disconnected from the ever-evolving world. In this talk, we will explore the challenge of updating these models without retraining them from scratch. We’ll examine current techniques such as fine-tuning, parameter-efficient methods (PEFT), Retrieval-Augmented Generation (RAG), and model editing approaches like Elastic Weight Consolidation (EWC), each with its own trade-offs in scalability, consistency, and memory retention.

But what comes next? Can LLMs evolve continuously, much like human learners? This talk will delve into the concept of incremental and continual learning for LLMs, why it’s challenging, what it entails, and how we might move toward systems that truly learn and adapt over time, without forgetting their past knowledge.

Bio:

Yash Kumar is a Postdoctoral Research Associate at the University of Virginia, where his research focuses on model editing, continual learning, and neural reasoning. His work focuses on developing efficient methods for updating large language models (LLMs) to refine knowledge, minimize hallucinations, and enable continuous adaptation without catastrophic forgetting. Yash holds a Ph.D. in Computer Science and Engineering from IIIT Delhi, where his research focused on abstractive text summarization.Looking forward to your participation.