[Theory] Fwd: [TTIC Talks] 2/11 TTIC Colloquium: Greg Durrett, University of Texas at Austin

[Avrim Blum via Theory]

Reminder of Greg Durrett's talk at 10:00am today.

Best,
Avrim

---------- Forwarded message ---------
From: Mary Marre <mmarre at ttic.edu>
Date: Tue, Feb 4, 2025 at 8:10 PM
Subject: [TTIC Talks] 2/11 TTIC Colloquium: Greg Durrett, University of
Texas at Austin
To: TTIC Talks <talks at ttic.edu>, <colloquium at cs.uchicago.edu>, <
theory at mailman.cs.uchicago.edu>, Shannon Jordan <shannonjordan at uchicago.edu>


*When:*        Tuesday, February 11, 2025 at* 10:00** am** CT   *


*Where:       *Talk will be given *live, in-person* at

                   TTIC, 6045 S. Kenwood Avenue

                   5th Floor, Room 530


*Virtually:*    *via panopto: **livestream*
<https://uchicago.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=0023003d-f434-41e5-9ef7-b27b001672c4>






*Who: *         Greg Durrett, University of Texas at Austin



*Title:          *Specializing LLMs for Reliability

*Abstract: *Large language models (LLMs) have advanced the frontiers of AI
reasoning: they can synthesize information from multiple sources, derive
new conclusions, and explain those conclusions to their users. However,
LLMs do not do this reliably. They hallucinate facts, convincingly state
incorrect deductions, and exhibit logical fallacies like confirmation bias.
In this talk, I will describe my lab's work on making LLM systems reliable
by introspecting their behavior. First, I will demonstrate that better
understanding of LLMs helps us train them to be more reliable reasoners.
Our work shows that model interpretation techniques can advance training
methodology and dataset curation for reasoning models. Second, I will argue
that automating fine-grained evaluation of LLM output provides a level of
understanding necessary for further progress. I will describe the
ingredients of effective automated evaluators and a state-of-the-art
factuality evaluation system, MiniCheck, showing that analyzing the nature
of hallucinations can help reduce them. Finally, I will describe how deeper
understanding of LLMs will let us tackle their most fundamental
limitations, such as their inconsistency when given different inputs. I
will propose how these pieces might soon be combined to form reliable AI
systems.

*Bio: *Greg Durrett is an associate professor of Computer Science at UT
Austin. His research is broadly in the areas of natural language processing
and machine learning. His group develops techniques for reasoning about
knowledge in text, verifying factuality of LLM generations, and
specializing LLMs to make them more reliable. He is a 2023 Sloan Research
Fellow and a recipient of a 2022 NSF CAREER award. His work has been
recognized by paper awards at EMNLP 2024 and EMNLP 2013. He was a founding
organizer of the Workshop on Natural Language Reasoning and Structured
Explanations at ACL 2023 and ACL 2024 and is a current member of the NAACL
board. He received his BS in Computer Science and Mathematics from MIT and
his PhD in Computer Science from UC Berkeley, where he was advised by Dan
Klein.

*Host: **David McAllester* <mcallester at ttic.edu>




Mary C. Marre
Faculty Administrative Support
*Toyota Technological Institute*
*6045 S. Kenwood Avenue, Rm 517*
*Chicago, IL  60637*
*773-834-1757*
*mmarre at ttic.edu <mmarre at ttic.edu>*
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