Dia: 8 de maig del 2024
Hora: de 12.00h a 13.00h
Lloc: aula 52.217, edifici Roc Boronat, Campus Poblenou UPF


Abstract:
Some of the most pressing issues with large language models (LLMs), such as the generation of factually incorrect text and logically incorrect reasoning, may be attributed to the way models represent and recall knowledge internally. In this talk, we will evaluate the representation and utilization of knowledge dependencies in LLMs from two different perspectives. First, we will consider the task of knowledge editing, showing that (a) using various editing methods to edit a specific fact does not implicitly modify other facts that depend on it, and (b) some facts are often hard to disentangle. Next, we will consider the setting of latent multi-hop reasoning, showing that LLMs only weakly rely on knowledge dependencies when answering complex queries. While these shortcomings could potentially be mitigated by intervening on the LLM computation, they call for better training procedures and possibly new architectures.

Bio:
Mor Geva is an Assistant Professor (Senior Lecturer) at the School of Computer Science at Tel Aviv University and a Visiting Researcher at Google Research. Her research focuses on understanding the inner workings of large language models, to increase their transparency and efficiency, control their operation, and improve their reasoning abilities. Mor completed a Ph.D. in Computer Science and a B.Sc. in Bioinformatics at Tel Aviv University, and was a postdoctoral researcher at Google DeepMind and the Allen Institute for AI. She was nominated as one of the MIT Rising Stars in EECS and is a laureate of the Séphora Berrebi Scholarship in Computer Science. She was awarded the Dan David Prize for graduate students in the field of AI and received an Outstanding Paper Award at EACL 2023.