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<b>Professor Eunsol Choi</b></div>
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<div style="direction: ltr;"><span style="font-family: Arial, Helvetica, sans-serif; font-size: 11pt; color: rgb(0, 0, 0);"><b>Assistant Professor</b></span></div>
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<b>Friday, February 23rd</b></div>
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<b>11:00am - 12:00pm</b></div>
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<b>In person: TTIC, 6045 S. Kenwood Avenue Room 529</b></div>
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<b>Zoom: <a href="https://uchicago.zoom.us/j/98297764499?pwd=ajNQSTZnMHRmMENkd1hjdjlNeW1xdz09#success" id="OWAad692c21-a030-99ae-de80-755ad4f37d87" class="OWAAutoLink" title="https://uchicago.zoom.us/j/98297764499?pwd=ajNQSTZnMHRmMENkd1hjdjlNeW1xdz09#success" data-loopstyle="linkonly" data-auth="NotApplicable">
https://uchicago.zoom.us/j/98297764499?pwd=ajNQSTZnMHRmMENkd1hjdjlNeW1xdz09#success</a></b></div>
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<b>Title: Knowledge- Rich Language Systems in a Dynamic World</b></div>
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<div style="direction: ltr;"><span style="font-family: Arial, sans-serif; font-size: 14.666667px; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255);"><b>Abstract</b>: Natural language interfaces provide an intuitive and powerful tool to assess rich
knowledge at scale. Modern language systems draw information from two rich knowledge sources: (1) information stored in its parameters during massive pretraining and (2) documents retrieved at inference time. Yet, we are far from building systems that can
reliably provide information from such knowledge sources. Existing systems frequently fail to identify relevant information and provide outdated or incorrect information. In this talk, I will discuss solutions for a more robust system. In the first part of
the talk, I will present a module for scaling retrieval-based knowledge augmentation. We learn a compressor that maps retrieved documents into textual summaries prior to in-context integration. This not only reduces the computational costs but also filters
irrelevant or incorrect information. In the second half of the talk, I will first discuss the challenges of updating knowledge stored in model parameters and propose a method to prevent models from reciting outdated information by identifying facts that are
prone to rapid change. I will conclude my talk by proposing an interactive system that can elicit information from users when needed. </span></div>
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<div style="direction: ltr;"><span style="font-family: Arial, sans-serif; font-size: 14.666667px; color: rgb(0, 0, 0); background-color: rgb(255, 255, 255);"><b>Bio</b>: Eunsol Choi is an assistant professor in the Computer Science department at the University
of Texas at Austin. Prior to UT, she spent a year at Google AI as a visiting researcher. Her research area spans natural language processing and machine learning. She is particularly interested in interpreting and reasoning about text in a dynamic real world
context. She is a recipient of a Facebook research fellowship, Google faculty research award, Sony faculty award, and an outstanding paper award at EMNLP. She received a Ph.D. in computer science and engineering from University of Washington and B.A in mathematics
and computer science from Cornell University.</span></div>
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<b>Abigail Santana</b></div>
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Business Assistant - Computer Science</div>
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The University of Chicago</div>
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5730 S Ellis Ave, JCL 212</div>
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Chicago, IL 60637</div>
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<span style="color: rgb(12, 100, 192);"><u>asantana01@uchicago.edu</u></span></div>
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Office: 773-834-2750</div>
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