BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Computer Science - ECPv6.17.5.1//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Department of Computer Science
X-ORIGINAL-URL:https://www.cs.jhu.edu
X-WR-CALDESC:Events for Department of Computer Science
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20240310T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20241103T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20250224T120000
DTEND;TZID=America/New_York:20250224T131500
DTSTAMP:20250214T175358Z
CREATED:20250214T175358Z
LAST-MODIFIED:20250214T175358Z
UID:1986294-1740398400-1740402900@www.cs.jhu.edu
SUMMARY:CS & CLSP Seminar Series: Enabling Language Models to Process Information at Scale
DESCRIPTION:Abstract\nLanguage models are highly effective at understanding and generating text\, holding immense potential as intuitive\, personalized interfaces for accessing information. Expanding their ability to gather and synthesize large volumes of information will further unlock transformative applications\, ranging from generative search engines to AI literature assistants. In this talk\, Tianyu Gao will present his research on advancing LMs for information processing at scale. First\, he will present his evaluation framework for LM-based information-seeking systems\, emphasizing the importance of providing citations for verifying the model-generated answers. His evaluation highlights shortcomings in LMs’ abilities to reliably process long-form texts (e.g.\, dozens of webpages)\, which he addresses by developing state-of-the-art long-context LMs that outperform leading industry efforts while using a small fraction of the computational budget. Gao will then introduce his foundational work on using contrastive learning to produce performant text embeddings\, which form the cornerstone of effective and scalable search. In addition to building systems that can process large-scale information\, he will discuss his contributions to creating efficient pre-training and adaptation methods for LMs\, which enable scalable deployment of LM-powered applications across diverse settings. Finally\, Gao will share his vision for the next generation of autonomous information processing systems and outline the foundational challenges that must be addressed to realize this vision. \nSpeaker Biography\nTianyu Gao is a fifth-year PhD student in the Department of Computer Science at Princeton University\, advised by Danqi Chen. His research focuses on developing principled methods for training and adapting language models\, many of which have been widely adopted across academia and industry. Driven by transformative applications such as using language models as information-seeking tools\, his work also advances robust evaluation and fosters a deeper understanding to guide the future development of language models. Gao led the first workshop on long-context foundation models at the 2024 International Conference on Machine Learning\, won an outstanding paper award at the 2022 Annual Meeting of the Association for Computational Linguistics \, and received an IBM PhD Fellowship Award in 2023. He received his BEng from Tsinghua University in 2020. \nZoom link >>
URL:https://www.cs.jhu.edu/event/cs-clsp-seminar-series-enabling-language-models-to-process-information-at-scale/
LOCATION:B-17 Hackerman Hall
CATEGORIES:Seminars and Lectures
END:VEVENT
END:VCALENDAR