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X-WR-CALNAME:Informative Example Selection for In-Context Learning
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UID:2024-02-12-shivanshu-gupta@cml.ics.uci.edu
DTSTAMP:20240212T000000Z
SEQUENCE:57690
DTSTART;TZID=America/Los_Angeles:20240212T130000
DTEND;TZID=America/Los_Angeles:20240212T140000
SUMMARY:[CML Seminar] Shivanshu Gupta: Informative Example Selection for In
-Context Learning
LOCATION:Donald Bren Hall 4011
DESCRIPTION:Shivanshu Gupta\, PhD Student\, Department of Computer Science\
, UC Irvine\n\nTitle: Informative Example Selection for In-Context Learnin
g\n\nAbstract: In-context Learning (ICL) uses large language models (LLMs)
for new tasks by conditioning them on prompts comprising a few task examp
les. With the rise of LLMs that are intractable to train or hidden behind
APIs\, the importance of such a training-free interface cannot be overstat
ed. However\, ICL is known to be critically sensitive to the choice of in-
context examples. Despite this\, the standard approach for selecting in-co
ntext examples remains to use general-purpose retrievers due to the limite
d effectiveness and training requirements of prior approaches. In this tal
k\, I'll posit that good in-context examples demonstrate the salient infor
mation necessary to solve a given test input. I'll present efficient appro
aches for selecting such examples\, with a special focus on preserving the
training-free ICL pipeline.\n\nhttps://cml.ics.uci.edu/seminars/2024-02-1
2-shivanshu-gupta
X-ALT-DESC;FMTTYPE=text/html:Shivanshu Gupta\, PhD Stude
nt\, Department of Computer Science\, UC Irvine
Title: Infor
mative Example Selection for In-Context Learning
Abstract: I
n-context Learning (ICL) uses large language models (LLMs) for new tasks b
y conditioning them on prompts comprising a few task examples. With the ri
se of LLMs that are intractable to train or hidden behind APIs\, the impor
tance of such a training-free interface cannot be overstated. However\, IC
L is known to be critically sensitive to the choice of in-context examples
. Despite this\, the standard approach for selecting in-context examples r
emains to use general-purpose retrievers due to the limited effectiveness
and training requirements of prior approaches. In this talk\, I'll posit t
hat good in-context examples demonstrate the salient information necessary
to solve a given test input. I'll present efficient approaches for select
ing such examples\, with a special focus on preserving the training-free I
CL pipeline.
https://cml.ics.uci.edu/seminars/2024-02-12-shivanshu-gupt
a
URL:https://cml.ics.uci.edu/seminars/2024-02-12-shivanshu-gupta
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