Tianyu Gao (Princeton)- Enabling Language Models to Process Information at Scale

Abstract: Language models (LMs) can effectively internalize knowledge from vast amounts of pre-training data, enabling them to achieve remarkable performance on exam-style benchmarks. Expanding their ability to compile, synthesize, and reason over large volumes of information on the fly will further unlock transformative applications, ranging from AI literature assistants to generative search engines. In this talk, I will present my research on advancing LMs for processing information at scale. (1) I will present my evaluation framework for LM-based information-seeking systems, emphasizing the importance of providing citations for verifying the model-generated answers. Our evaluation highlights shortcomings in LMs’ abilities to reliably process long-form texts (e.g., dozens of webpages), which I address by developing state-of-the-art long-context LMs that outperform leading industry efforts while using a small fraction of the computational budget. (2) I will then introduce my foundational work on using contrastive learning to produce high-performing text embeddings, which form the cornerstone of effective and scalable search. (3) In addition to building systems that can process large-scale information, I will discuss my contributions to creating efficient pre-training and customization methods for LMs, which enable scalable deployment of LM-powered applications across diverse settings. Finally, I will share my vision for the next generation of autonomous information processing systems and outline the foundational challenges that must be addressed to realize this vision.