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SUMMARY:Retrieving and Sampling Diverse Outputs - Prof. Eunsol Choi (NYU)
DTSTART:20251113T140000Z
DTEND:20251113T150000Z
UID:TALK238918@talks.cam.ac.uk
CONTACT:Shun Shao
DESCRIPTION:Abstract: Real-world user queries often contain questions that
  admit a wide range of valid answers without a single ground truth. Howeve
 r\, large language models (LLMs) often struggle to generate diverse and co
 mprehensive responses. In this talk\, we will discuss two paths towards th
 is goal\, (1) retrieving a diverse set of documents and (2) sampling a lar
 ge number of responses from LLMs. In the first part of the talk\, I will f
 irst quantify the limitations of existing dense retrievers which generate 
 one query vector. Many strong retrievers all struggle when the gold docume
 nt set contains dissimilar targets. To address this\, we present a new ret
 riever architecture that autoregressively generates multiple\, distinct qu
 ery vectors\, and each query vector is used to retrieve documents from the
  corpus. In the second part of the talk\, I will discuss inference strateg
 ies for sampling diverse outputs from LLMs. Prompting LLMs to sequentially
  generate a diverse set of answers works well for simpler factoid queries\
 , but is less effective for more complex queries. We further explore mergi
 ng outputs from multiple LLMs\, showing its potential and challenges. I wi
 ll conclude by discussing a multi-turn agentic framework interleaving retr
 ieval and generation from LLMs to craft a comprehensive answer.  \n\n\nBio
 : Eunsol Choi is an assistant professor of computer science and data scien
 ce at New York University. Her research spans natural language processing 
 and machine learning\, with a focus on interpreting and reasoning about te
 xt in dynamic real-world contexts. Prior to joining NYU\, she was an assis
 tant professor at the University of Texas at Austin and a visiting researc
 her at Google. She holds a Ph.D. in computer science and engineering from 
 the University of Washington. She is a recipient of a Facebook research fe
 llowship\, Google faculty research award\, Sony faculty award\, NSF CAREER
  award and an outstanding paper award at EMNLP.
LOCATION:https://cam-ac-uk.zoom.us/j/97599459216?pwd=QTRsOWZCOXRTREVnbTJBd
 XVpOXFvdz09
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