Abstract:
Large Language Models (LLMs) have dramatically transformed the landscape of Generative AI, making profound impacts across a broad spectrum of domains. From enhancing Recommender Systems to advancing the frontiers of Natural Language Processing (NLP), LLMs have become indispensable. Their versatility extends into specialized sectors, such as finance with the development of BloombergGPT, and healthcare through MedLlama, showcasing their adaptability and potential for industry-specific innovations.

In this presentation, we will embark on a comprehensive exploration of the evolution of Large Language Models. Our journey will trace the origins of LLMs, highlighting key milestones and breakthroughs, and proceed to examine the latest advancements and research directions in the field. To mirror the structured and layered nature of LLMs themselves, our discussion will be organized into distinct sections. We'll begin with the foundational aspect of prompting, delve into the intricacies of their architecture, and discuss pivotal strategies such as Pretraining, Fine-tuning, and Parameter Efficient Fine-Tuning (PEFT). Furthermore, we'll address the challenges and solutions related to the mitigation of hallucination, a critical aspect of ensuring the reliability and accuracy of LLM-generated content.

Speaker Bio:

Vinija Jain brings to the table an extensive background in machine learning, with significant expertise in developing recommender systems at Amazon and spearheading NLP initiatives at Oracle. Her passion for artificial intelligence was ignited during her time in the Stanford AI program, which served as a catalyst for her deep dive into the field. Currently, Vinija is actively engaged in fundamental research and collaborates with the Artificial Intelligence Institute of South Carolina (AIISC) at the University of South Carolina. Her latest work with AIISC on AI-Generated Text Detection has been recognized with an outstanding paper award at EMNLP '23, underscoring her contributions to advancing AI research and application

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