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SUMMARY:LLMs: Everything’s Different and Nothing Has Changed - Emma Stru
 bell\, CMU
DTSTART:20240301T120000Z
DTEND:20240301T130000Z
UID:TALK211804@talks.cam.ac.uk
CONTACT:Eric Chamoun
DESCRIPTION:The field of NLP is in the midst of a disruptive shift\, fuele
 d most recently by the advent of large language models (LLMs)\, with impac
 ts on our methodologies\, funding and public perception. While the core te
 chnologies and scope of real-world impact of our field may be changing (ev
 erything is different!)\, many of the same key challenges faced since the 
 inception of our field remain (nothing has changed). In this talk I’ll d
 escribe recent work characterizing and tackling some of these challenges\,
  notably: data-efficient domain adaptation and lifelong learning. I will a
 lso anchor discussion of cycles and shifts in the field by describing find
 ings from a qualitative study of factors shaping the community over time\,
  including culture\, incentives\, and infrastructure. Through these comple
 mentary lenses into the past\, present and future\, I aim to inspire share
 d hope\, excitement and discussion.\n\nBio:\n\nEmma Strubell is the Raj Re
 ddy Assistant Professor in the Language Technologies Institute in the Scho
 ol of Computer Science at Carnegie Mellon University\, and a Visiting Scie
 ntist at the Allen Institute for Artificial Intelligence. Previously she h
 eld research scientist roles at Google and FAIR after earning her doctoral
  degree in 2019 from the University of Massachusetts Amherst. Her research
  lies at the intersection of natural language processing and machine learn
 ing\, with a focus on providing pragmatic solutions to practitioners who w
 ish to gain insights from natural language text via computation- and data-
 efficient AI. Her work has been recognized with a Madrona AI Impact Award\
 , best paper awards at ACL and EMNLP\, and cited in news outlets including
  the New York Times and Wall Street Journal.
LOCATION:Computer Lab\, SS03
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