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X-WR-CALNAME:Enabling Language Models to Process Information at Scale
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UID:2025-03-13-tianyu-gao@cml.ics.uci.edu
DTSTAMP:20250313T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20250313T110000
DTEND;TZID=America/Los_Angeles:20250313T120000
SUMMARY:[CML Seminar] Tianyu Gao: Enabling Language Models to Process Infor
mation at Scale
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Tianyu Gao\, PhD Student\, Department of Computer Science\, Pri
nceton University\n\nTitle: Enabling Language Models to Process Informatio
n at Scale\n\nAbstract: Language models (LMs) can effectively internalize
knowledge from vast amounts of pre-training data\, enabling them to achiev
e 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-seeki
ng systems\, emphasizing the importance of providing citations for verifyi
ng the model-generated answers. (2) I will then introduce my foundational
work on using contrastive learning to produce high-performing text embeddi
ngs\, 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 custo
mization methods for LMs. Finally\, I will share my vision for the next ge
neration of autonomous information processing systems.\n\nhttps://cml.ics.
uci.edu/seminars/2025-03-13-tianyu-gao
X-ALT-DESC;FMTTYPE=text/html:Tianyu Gao\, PhD Student\,
Department of Computer Science\, Princeton University
Title:
Enabling Language Models to Process Information at Scale
Abstra
ct: Language models (LMs) can effectively internalize knowledge from v
ast amounts of pre-training data\, enabling them to achieve remarkable per
formance on exam-style benchmarks. Expanding their ability to compile\, sy
nthesize\, and reason over large volumes of information on the fly will fu
rther unlock transformative applications\, ranging from AI literature assi
stants to generative search engines. In this talk\, I will present my rese
arch on advancing LMs for processing information at scale. (1) I will pres
ent my evaluation framework for LM-based information-seeking systems\, emp
hasizing the importance of providing citations for verifying the model-gen
erated answers. (2) I will then introduce my foundational work on using co
ntrastive learning to produce high-performing text embeddings\, which form
the cornerstone of effective and scalable search. (3) In addition to buil
ding systems that can process large-scale information\, I will discuss my
contributions to creating efficient pre-training and customization methods
for LMs. Finally\, I will share my vision for the next generation of auto
nomous information processing systems.
https://cml.ics.uci.edu/seminars/2025
-03-13-tianyu-gao
URL:https://cml.ics.uci.edu/seminars/2025-03-13-tianyu-gao
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