Gordon Lecture: Dr. Ling Liu

The human-like generative ability of LLMs has ushered in a new era of foundational models and generative AI (genAI), unlocking new possibilities and driving cross-domain innovations. However, the transformative potential of these genAI models is hindered by significant accessibility challenges: powered by over-parameterization, LLMs are requiring hundreds of GBs of GPU memory for learning and inference, hence facing deployment challenges on heterogeneous platforms and on learning for downstream tasks with proprietary data, making equitable accessibility of genAI for all a grand challenge. Large genAI models trained on massive public domain data may introduce problematic hallucinations, which can lead to misinformation and biased outcomes in mission-critical applications, making responsible adaptation of genAI models another grand challenge. 

This Gordon Lecture will present a responsible and resource efficient framework for adapting genAI to domain-specific learning, aiming to tackle the above mentioned two accessibility challenges. Ling Liu will first review the pros and cons of existing augmentation generation techniques and introduce the multi-genAI-agent collaboration frameworks for responsible deployment of genAI in mission critical applications. Liu will also describe scalable and resource-efficient fine-tuning framework and optimizations for learning downstream tasks.

Liu is a professor in the School of Computer Science at Georgia Institute of Technology. She directs the research programs in the Distributed Data Intensive Systems Lab (DiSL), examining various aspects of Internet - scale big data powered artificial intelligence (AI) systems, algorithms and analytics, including performance, reliability, privacy, security and trust.