Dr. Sherry Tongshuang Wu
Practical AI Systems: From General-Purpose AI to (the Right) Specific Use Cases
AI research has made great strides in developing general-purpose models (e.g., LLMs) that can excel across a wide range of tasks, enabling users to explore AI applications tailored to their unique needs without the complexities of custom model training. However, with the opportunities come the challenges — General-purpose models prioritize overall performance, but this can neglect specific user needs. How can we make these models practically usable? In this talk, I will present our recent work on assessing and tailoring general-purpose models for specific use cases. I will first cover methods for evaluating and mapping LLMs to specific usage scenarios, then reflect on the importance of identifying the right tasks for LLMs by comparing how humans and LLMs may perform the same tasks differently. In my final remarks, I will discuss the potential of training humans and LLMs with complementary skill sets.
Bio
Sherry Tongshuang Wu is an Assistant Professor in the Human-Computer Interaction Institute at Carnegie Mellon University. Her research lies at the intersection of Human-Computer Interaction and Natural Language Processing, and primarily focuses on how humans (AI experts, lay users, domain experts) can practically interact with (debug, audit, and collaborate with) AI systems. To this end, she has worked on assessing NLP model capabilities, supporting human-in-the-loop NLP model debugging and correction, as well as facilitating human-AI collaboration. She has authored award-winning papers in top-tier NLP, HCI and Visualization conferences and journals such as ACL, CHI, TOCHI, TVCG, etc. Before joining CMU, Sherry received her Ph.D. degree from the University of Washington and her bachelor degree from the Hong Kong University of Science and Technology, and has interned at Microsoft Research, Google Research, and Apple. You can find out more about her.
