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BEGIN:VEVENT

CATEGORIES:Lectures/Conferences
CATEGORIES:Utilities
CATEGORIES:Lecture/Talk
CATEGORIES:Panel/Seminar/Colloquium
CATEGORIES:Technology
CATEGORIES:Main
CONTACT;X-BEDEWORK-UID=00f1fcdb-0f068baf-010f-068baf83-00000004:None
CREATED:20240213T201459Z
DESCRIPTION:LUNCH \nLunch will be served at 11:45 AM. \n\nABSTRACT \nAs ma
 ssive language models (LMs) like GPT-4 dominate natural language processi
 ng and AI\, extreme-scale has become a clear and frequent theme for succe
 ss. However\, increasing model size is inherently at odds with the intere
 sts of a diverse user base and community of open researchers. The largest
  models are typically closed to the public\, extremely energy-intensive\,
  and difficult to conduct systematic and reproducible research on. \n\nIn
  this talk\, I will discuss my vision for effective natural language proc
 essing beyond scale alone. I will begin by describing alternative approac
 hes\, more efficient methods that work with compact language models to un
 lock hidden capabilities. I begin with inference-time algorithms that wor
 k on top of existing models to allow new functionality. Next\, I describe
  a method for distilling valuable knowledge from extreme-scale models int
 o compact LMs. Finally\, I will explain my work towards understanding the
  limits that even extreme-scale language models suffer from\, with a part
 icular focus on how such models differ from human intuitions. \n\nSPEAKER
  BIO \nPeter West is PhD student in the Paul G. Allen School of Computer 
 Science & Engineering at the University of Washington\, working with Yeji
 n Choi. His research is focused on natural language processing and langua
 ge models\, particularly studying the capabilities and limits of both com
 pact and extreme-scale models. His work has received multiple awards\, in
 cluding best methods paper at NAACL 2022\, and outstanding paper awards a
 t ACL and EMNLP in 2023. His work has been supported in part by an NSERC 
 PGS-D fellowship. Previously\, Peter received a BSc in computer science f
 rom the University of British Columbia.
DURATION:PT1H
DTSTAMP:20240213T201459Z
DTSTART;TZID=America/New_York:20240304T120000
LAST-MODIFIED:20240213T201459Z
LOCATION;X-BEDEWORK-UID=18832edc-1bffd55f-011c-2ea4458d-000003df:LSRC D106
 
STATUS:CONFIRMED
SUMMARY:Discovering Hidden Capabilities and Limits in Large Language Model
 s
UID:CAL-8a0292fd-8d13410f-018d-a41bc60d-000075e1demobedework@mysite.edu
URL:https://cs.duke.edu/events/discovering-hidden-capabilities-and-limits-
 large-language-models
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X-BEDEWORK-SPEAKER:Peter West\, UW-CSE
X-BEDEWORK-DUKE-SERIES:Duke Computer Science Colloquium
X-BEDEWORK-STUDENT-CONTACT;X-BEDEWORK-PARAM-EMAIL=tomasi@duke.edu:Carlo To
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