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X-WR-CALNAME:Hidden Capabilities and Counterintuitive Limits in Large Langu
age Models
X-WR-TIMEZONE:America/Los_Angeles
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UID:2024-04-15-peter-west@cml.ics.uci.edu
DTSTAMP:20240415T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20240415T110000
DTEND;TZID=America/Los_Angeles:20240415T120000
SUMMARY:[CML Seminar] Peter West: Hidden Capabilities and Counterintuitive
Limits in Large Language Models
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Peter West\, PhD Student\, School of Computer Science & Enginee
ring\, University of Washington\n\nTitle: Hidden Capabilities and Counteri
ntuitive Limits in Large Language Models\n\nAbstract: Massive scale has be
en a recent winning recipe in natural language processing and AI\, with ex
treme-scale language models like GPT-4 receiving most attention. This is i
n spite of staggering energy and monetary costs\, and further\, the contin
uing struggle of even the largest models with concepts such as composition
al problem solving and linguistic ambiguity. In this talk\, I will propose
my vision for a research landscape where compact language models share th
e forefront with extreme scale models\, working in concert with many piece
s besides scale\, such as algorithms\, knowledge\, information theory\, an
d more. I will cover alternative ingredients to scale\, and discuss counte
rintuitive disparities in the capabilities of even extreme-scale models\,
which can meet or exceed human performance in some complex tasks while tra
iling behind humans in what seem to be much simpler tasks.\n\nhttps://cml.
ics.uci.edu/seminars/2024-04-15-peter-west
X-ALT-DESC;FMTTYPE=text/html:Peter West\, PhD Student\,
School of Computer Science &\; Engineering\, University of Washington
Title: Hidden Capabilities and Counterintuitive Limits in Lar
ge Language Models
Abstract: Massive scale has been a recent
winning recipe in natural language processing and AI\, with extreme-scale
language models like GPT-4 receiving most attention. This is in spite of
staggering energy and monetary costs\, and further\, the continuing strugg
le of even the largest models with concepts such as compositional problem
solving and linguistic ambiguity. In this talk\, I will propose my vision
for a research landscape where compact language models share the forefront
with extreme scale models\, working in concert with many pieces besides s
cale\, such as algorithms\, knowledge\, information theory\, and more. I w
ill cover alternative ingredients to scale\, and discuss counterintuitive
disparities in the capabilities of even extreme-scale models\, which can m
eet or exceed human performance in some complex tasks while trailing behin
d humans in what seem to be much simpler tasks.
https://cml.ics.uci.edu/semi
nars/2024-04-15-peter-west
URL:https://cml.ics.uci.edu/seminars/2024-04-15-peter-west
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