Difference between revisions of "Meeting Schedule"

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'''Abstract:''' The fields of Artificial Intelligence and Natural Language Processing have been revolutionized by the advent  of  Large  Language  Models  such  as  GPT4.  They  are  perceived  as  being  language  experts and there is a lot of speculation about how intelligent they are, with claims being made about “Sparks of  General  Artificial  Intelligence.”  This  talk  will  describe  in  detail  an  English  linguistic  construction, the Caused Motion Construction, and compare prior interpretation approaches with current LLM interpretations.  The  prior  approaches  are  based  on  VerbNet. It’s unique  contributions  to  prior  approaches  will  be  outlined.  Then  the  results  of  a  recent  preliminary study  probing  GPT4’s  analysis  of  the  same  constructions  will  be  presented.  Not  surprisingly,  this analysis  illustrates  both  strengths  and  weaknesses  of  GPT4’s  ability  to  interpret  Caused  Motion Constructions and to generalize this interpretation.
 
'''Abstract:''' The fields of Artificial Intelligence and Natural Language Processing have been revolutionized by the advent  of  Large  Language  Models  such  as  GPT4.  They  are  perceived  as  being  language  experts and there is a lot of speculation about how intelligent they are, with claims being made about “Sparks of  General  Artificial  Intelligence.”  This  talk  will  describe  in  detail  an  English  linguistic  construction, the Caused Motion Construction, and compare prior interpretation approaches with current LLM interpretations.  The  prior  approaches  are  based  on  VerbNet. It’s unique  contributions  to  prior  approaches  will  be  outlined.  Then  the  results  of  a  recent  preliminary study  probing  GPT4’s  analysis  of  the  same  constructions  will  be  presented.  Not  surprisingly,  this analysis  illustrates  both  strengths  and  weaknesses  of  GPT4’s  ability  to  interpret  Caused  Motion Constructions and to generalize this interpretation.
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Recording: https://o365coloradoedu-my.sharepoint.com/:v:/r/personal/mpalmer_colorado_edu/Documents/BoulderNLP-Palmer-Oct2-2024.mp4?csf=1&web=1&nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJPbmVEcml2ZUZvckJ1c2luZXNzIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXciLCJyZWZlcnJhbFZpZXciOiJNeUZpbGVzTGlua0NvcHkifX0&e=aCHeN8
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Revision as of 16:12, 18 October 2024

Location: Hybrid - Muenzinger D430, and the zoom link below

Time: Wednesdays at 11:30am, Mountain Time

Zoom link: https://cuboulder.zoom.us/j/97014876908

Date Title
08/28/2024 Planning, introductions, welcome!
09/04/2024 Brunch Social
09/11/2024 Watch and discuss NLP keynote

Winner: Barbara Plank’s “Are LLMs Narrowing our Horizon? Let’s Embrace Variation in NLP!”

09/18/2024 CLASIC presentations
09/25/2024 Invited talks/discussions from Leeds and Anschutz folks: Liu Liu, Abe Handler, Yanjun Gao, Curry Guinn


10/02/2024 Martha Palmer, Annie Zaenen, Susan Brown, Alexis Cooper.

Title: Testing GPT4's interpretation of the Caused-Motion Construction

Abstract: The fields of Artificial Intelligence and Natural Language Processing have been revolutionized by the advent of Large Language Models such as GPT4. They are perceived as being language experts and there is a lot of speculation about how intelligent they are, with claims being made about “Sparks of General Artificial Intelligence.” This talk will describe in detail an English linguistic construction, the Caused Motion Construction, and compare prior interpretation approaches with current LLM interpretations. The prior approaches are based on VerbNet. It’s unique contributions to prior approaches will be outlined. Then the results of a recent preliminary study probing GPT4’s analysis of the same constructions will be presented. Not surprisingly, this analysis illustrates both strengths and weaknesses of GPT4’s ability to interpret Caused Motion Constructions and to generalize this interpretation.

Recording: https://o365coloradoedu-my.sharepoint.com/:v:/r/personal/mpalmer_colorado_edu/Documents/BoulderNLP-Palmer-Oct2-2024.mp4?csf=1&web=1&nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJPbmVEcml2ZUZvckJ1c2luZXNzIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXciLCJyZWZlcnJhbFZpZXciOiJNeUZpbGVzTGlua0NvcHkifX0&e=aCHeN8


10/09/2024 NAACL Paper Clinic: Come get feedback on your submission drafts!
10/16/2024 Senior Thesis Proposals:


Alexandra Barry

Title: Benchmarking LLM Handling of Cross-Dialectal Spanish

Abstract: This proposal introduces current issues and gaps in cross-dialectal NLP in Spanish as well as the lack of resources available for Latin American dialects. The presentation will cover past work in dialect detection, translation, and benchmarking in order to build a foundation for a proposal that aims to create a benchmark that analyses LLM robustness across a series of tasks in different Spanish dialects


Tavin Turner

Title: Agreeing to Disagree: Statutory Relational Stance Modeling

Abstract: Policy division deeply affects which bills get passed in legislature, and how. So far, statutory NLP has predicted voting breakdowns, interpreted stakeholder benefit, informed legal decision support systems, and much more. In practice, legislation demands compromise and concession to pass important policy, yet models often struggle to reason over the whole act. Leveraging neuro-symbolic models, we seek to intermediate this challenge with relational structures of statutes’ sectional stances – modeling stance agreement, exception, etc. Beyond supporting downstream statutory analysis tasks, these structures could help stakeholders understand how a bill impacts them, litmus the cooperation within a legislature, and reveal patterns of compromise that aid a bill through ratification.

10/23/2024 Ananya's Proposal
10/30/2024 Marie's prelim
11/06/2024 Kevin Stowe - on Zoom
11/13/2024 Abteen's proposal
11/20/2024 Adam's defense
11/27/2024 No meeting: Fall break
12/04/2024 Enora's prelim
12/11/2024 DJ's prelim


Past Schedules