Difference between revisions of "Meeting Schedule"
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− | '''Location:''' Hybrid - | + | '''Location:''' Hybrid - Muenzinger D430, and the zoom link below |
− | '''Time:''' Wednesdays at | + | '''Time:''' Wednesdays at 11:30am, Mountain Time |
'''Zoom link:''' https://cuboulder.zoom.us/j/97014876908 | '''Zoom link:''' https://cuboulder.zoom.us/j/97014876908 | ||
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|- style="border-top: 2px solid DarkGray;" | |- style="border-top: 2px solid DarkGray;" | ||
− | | | + | | 08/28/2024 || '''Planning, introductions, welcome!''' |
|- style="border-top: 2px solid DarkGray;" | |- style="border-top: 2px solid DarkGray;" | ||
− | | | + | | 09/04/2024 || Brunch Social |
|- style="border-top: 2px solid DarkGray;" | |- style="border-top: 2px solid DarkGray;" | ||
− | | | + | | 09/11/2024 || Watch and discuss NLP keynote |
+ | |||
+ | '''Winner:''' Barbara Plank’s “Are LLMs Narrowing our Horizon? Let’s Embrace Variation in NLP!” | ||
|- style="border-top: 2px solid DarkGray;" | |- style="border-top: 2px solid DarkGray;" | ||
− | | | + | | 09/18/2024 || CLASIC presentations |
|- style="border-top: 2px solid DarkGray;" | |- style="border-top: 2px solid DarkGray;" | ||
− | | | + | | 09/25/2024 || Invited talks/discussions from Leeds and Anschutz folks: Liu Liu, Abe Handler, Yanjun Gao, Curry Guinn |
|- style="border-top: 2px solid DarkGray;" | |- style="border-top: 2px solid DarkGray;" | ||
− | | 02 | + | | 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 | |
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− | + | |- style="border-top: 2px solid DarkGray;" | |
− | + | | 10/09/2024 || NAACL Paper Clinic: Come get feedback on your submission drafts! | |
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|- style="border-top: 2px solid DarkGray;" | |- style="border-top: 2px solid DarkGray;" | ||
− | | | + | | 10/16/2024 || Senior Thesis Proposals: |
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− | ''' | + | '''Alexandra Barry''' |
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− | + | '''Title''': Benchmarking LLM Handling of Cross-Dialectal Spanish | |
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− | ''' | + | '''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 |
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− | + | '''Tavin Turner''' | |
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− | '''Title | + | '''Title''': Agreeing to Disagree: Statutory Relational Stance Modeling |
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− | '' | ||
+ | '''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. | ||
|- style="border-top: 2px solid DarkGray;" | |- style="border-top: 2px solid DarkGray;" | ||
− | | | + | | 10/23/2024 || '''Ananya Ganesh''''s PhD Dissertation Proposal |
− | + | '''Title''': Reliable Language Technology for Classroom Dialog Understanding | |
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− | + | '''Abstract''': In this proposal, I will lay out how NLP models can be developed to address realistic use cases in analyzing classroom dialogue. Towards this goal, I will first introduce a new task and corresponding dataset, focused on detecting off-task utterances in small-group discussions. I will | |
− | + | then propose a method to solve this task that considers how the inherent structure in the dialog can be used to learn richer representations of the dialog context. Next, I will introduce preliminary work on applying LLMs in the in-context learning setting for a broad range of tasks pertaining to qualitative coding of classroom dialog, and discuss potential follow-up work. Finally, keeping in mind our goals of serving many independent stakeholders, I will propose a study to incorporate differing stake-holder’s subjective judgments while curating gold-standard data for classroom discourse analysis. | |
|- style="border-top: 2px solid DarkGray;" | |- style="border-top: 2px solid DarkGray;" | ||
− | | | + | | 10/30/2024 || '''Marie McGregor''''s area exam |
− | '''Title:''' | + | '''Title''': Adapting AMR Metrics to UMR Graphs |
− | + | ||
+ | '''Abstract''': Uniform Meaning Representation (UMR) expands on the capabilities of Abstract Meaning Representation (AMR) by supporting document-level annotation, suitability for low-resource languages, and support for logical inference. As a framework for any sort of representation is developed, a way to measure the similarities or differences between two representations must be developed in tandem to support the creation of parsers and for computing inner-annotator agreement (IAA). Fortunately, there exists robust research into metrics to assess the similarity of AMR graphs. The usefulness of these metrics to UMRs depends on four key aspects: scalability, correctness, interpretability, and cross-lingual suitability. This paper investigates the applicability of AMR metrics to UMR graphs along these aspects in order to create useful and reliable UMR metrics. | ||
+ | |- style="border-top: 2px solid DarkGray;" | ||
+ | | 11/06/2024 || Kevin Stowe - on Zoom | ||
|- style="border-top: 2px solid DarkGray;" | |- style="border-top: 2px solid DarkGray;" | ||
− | | | + | | 11/13/2024 || Invited talk by Nick Dronen and Seminar Lunch |
− | + | |- style="border-top: 2px solid DarkGray;" | |
+ | | 11/20/2024 || Abteen's proposal | ||
− | ''' | + | |- style="border-top: 2px solid DarkGray;" |
+ | | 11/27/2024 || '''No meeting:''' Fall break | ||
|- style="border-top: 2px solid DarkGray;" | |- style="border-top: 2px solid DarkGray;" | ||
− | | | + | | 12/04/2024 || Enora's prelim |
|- style="border-top: 2px solid DarkGray;" | |- style="border-top: 2px solid DarkGray;" | ||
− | | | + | | 12/11/2024 || DJ's prelim |
+ | |- style="border-top: 2px solid DarkGray;" | ||
+ | | 1/23/25|| Chenhao Tan CS Colloquium, 3:30pm | ||
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=Past Schedules= | =Past Schedules= | ||
+ | * [[Spring 2024 Schedule]] | ||
* [[Fall 2023 Schedule]] | * [[Fall 2023 Schedule]] | ||
* [[Spring 2023 Schedule]] | * [[Spring 2023 Schedule]] |
Latest revision as of 11:32, 29 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.
|
10/09/2024 | NAACL Paper Clinic: Come get feedback on your submission drafts! |
10/16/2024 | Senior Thesis Proposals:
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 Ganesh's PhD Dissertation Proposal
Title: Reliable Language Technology for Classroom Dialog Understanding Abstract: In this proposal, I will lay out how NLP models can be developed to address realistic use cases in analyzing classroom dialogue. Towards this goal, I will first introduce a new task and corresponding dataset, focused on detecting off-task utterances in small-group discussions. I will then propose a method to solve this task that considers how the inherent structure in the dialog can be used to learn richer representations of the dialog context. Next, I will introduce preliminary work on applying LLMs in the in-context learning setting for a broad range of tasks pertaining to qualitative coding of classroom dialog, and discuss potential follow-up work. Finally, keeping in mind our goals of serving many independent stakeholders, I will propose a study to incorporate differing stake-holder’s subjective judgments while curating gold-standard data for classroom discourse analysis. |
10/30/2024 | Marie McGregor's area exam
Title: Adapting AMR Metrics to UMR Graphs Abstract: Uniform Meaning Representation (UMR) expands on the capabilities of Abstract Meaning Representation (AMR) by supporting document-level annotation, suitability for low-resource languages, and support for logical inference. As a framework for any sort of representation is developed, a way to measure the similarities or differences between two representations must be developed in tandem to support the creation of parsers and for computing inner-annotator agreement (IAA). Fortunately, there exists robust research into metrics to assess the similarity of AMR graphs. The usefulness of these metrics to UMRs depends on four key aspects: scalability, correctness, interpretability, and cross-lingual suitability. This paper investigates the applicability of AMR metrics to UMR graphs along these aspects in order to create useful and reliable UMR metrics. |
11/06/2024 | Kevin Stowe - on Zoom |
11/13/2024 | Invited talk by Nick Dronen and Seminar Lunch |
11/20/2024 | Abteen's proposal |
11/27/2024 | No meeting: Fall break |
12/04/2024 | Enora's prelim |
12/11/2024 | DJ's prelim |
1/23/25 | Chenhao Tan CS Colloquium, 3:30pm
|
Past Schedules
- Spring 2024 Schedule
- Fall 2023 Schedule
- Spring 2023 Schedule
- Fall 2022 Schedule
- Spring 2022 Schedule
- Fall 2021 Schedule
- Spring 2021 Schedule
- Fall 2020 Schedule
- Spring 2020 Schedule
- Fall 2019 Schedule
- Spring 2019 Schedule
- Fall 2018 Schedule
- Summer 2018 Schedule
- Spring 2018 Schedule
- Fall 2017 Schedule
- Summer 2017 Schedule
- Spring 2017 Schedule
- Fall 2016 Schedule
- Spring 2016 Schedule
- Fall 2015 Schedule
- Spring 2015 Schedule
- Fall 2014 Schedule
- Spring 2014 Schedule
- Fall 2013 Schedule
- Summer 2013 Schedule
- Spring 2013 Schedule
- Fall 2012 Schedule
- Spring 2012 Schedule
- Fall 2011 Schedule
- Summer 2011 Schedule
- Spring 2011 Schedule
- Fall 2010 Schedule
- Summer 2010 Schedule
- Spring 2010 Schedule
- Fall 2009 Schedule
- Summer 2009 Schedule
- Spring 2009 Schedule
- Fall 2008 Schedule
- Summer 2008 Schedule