CLSP Fall Seminar Series: Daniel Khashabi

Description

Daniel Khashabi, an assistant professor in computer science at Johns Hopkins, will present a Center for Language and Speech Processing seminar titled "Building More Helpful Language Models."

Abstract:

The arms race to build increasingly larger, powerful language models (LMs) in the past year has been remarkable. Yet incorporating LMs effectively into practical applications that facilitate manual workflows remains challenging. I will discuss LMs' limiting factors and our efforts to overcome them. I will start with challenges surrounding efficient and robust LM alignment. I will share insights from our recent paper "Self-Instruct" (ACL 2023), where we used vanilla (unaligned) LMs for aligning itself, an approach that has yielded some success. Then, I will move on to the challenge of tracing the output of LMs to reliable sources, a weakness that makes them prone to hallucinations. I will discuss our recent approach of 'according-to' prompting, which steers LMs to quote directly from sources observed in its pre-training. If time permits, I will discuss our ongoing project to adapt LMs to interact with web pages. Throughout the presentation, I will highlight our progress, and end with questions about our future progress.