Large language models (LLMs) are becoming increasingly used in various downstream applications not only in natural language processing but also in various other domains including computer vision, reinforcement learning, and drug discovery to name a few. What are the effects of using LLMs as task solvers? What kind of knowledge can an LLM encode? Can they efficiently use all the encoded knowledge while learning a downstream task? Are LLMs susceptible to the usual catastrophic forgetting while learning many tasks? How do we identify the biases that these LLMs encode and how do we eliminate those biases? In this talk, I will present an overview of several research projects in my lab that attempts to answer all these questions.