ABSTRACT: 

The rapid progress made over the last few years in generating linguistically coherent natural language has blurred, in the mind of many, the difference between natural language generation, understanding, and the ability to reason with respect to the world. Nevertheless, robust support of high-level decisions that depend on natural language understanding, and one that requires dealing with “truthfulness” are still beyond our capabilities, partly since most of these tasks are very sparse, often require grounding, and may depend on new types of supervision signals.

I will discuss some of the challenges underlying reasoning and argue that we should focus on LLMs as orchestrators – coordinating and managing multiple models, applications, and services, as a way to execute complex tasks and processes. I will discuss some of the challenges and present some of our work in this space, focusing on supporting task decomposition and planning.

ZOOM LINK (if unable to attend in-person): https://upenn.zoom.us/j/92067502115