Recent advances in deep learning algorithms and large-scale datasets are spurring progress in many Natural Language Processing (NLP) tasks, including question answering. Nevertheless, these models cannot scale up when task-annotated training data are scarce. This talk presents my lab's work toward building general-purpose models in NLP and how to systematically evaluate them. I present a new meta-dataset – called super-Natural Instructions – that includes a variety of NLP tasks and their descriptions to evaluate cross-task generalization. Then, I introduce a new meta training approach that can solve more than 1600 NLP tasks only from their descriptions and a few examples. Finally, I present a series of work in robust fine-tuning methods and how to edit models with arithmetics over task vectors.
Hanna Hajishirzi is an Associate Professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington and a Research Fellow at the Allen Institute for AI. Her research spans different areas in NLP and AI, focusing on developing machine learning algorithms that represent, comprehend, and reason about diverse forms of data at large scale. Applications for these algorithms include question answering, reading comprehension, representation learning, green AI, knowledge extraction, and conversational dialogue. Honors include the NSF CAREER Award, Sloan Fellowship, Allen Distinguished Investigator Award, Intel rising star award, multiple best paper and honorable mention paper awards, and several industry research faculty awards. Hanna received her PhD from University of Illinois and spent a year as a postdoc at Disney Research and CMU.