Meeting Schedule

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Location: Hybrid - Buchanan 430, and the zoom link below

Time: Wednesdays at 10:30am, Mountain Time

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

Date Title
01/24/2024 Planning, introductions, welcome!
01/31/2024 Brunch Social
02/07/2024 No Meeting - Virtual PhD Open House
02/14/2024 ACL paper clinic
02/21/2024 Cancelled in favor of LING Circle talk by Professor Gibbs


02/28/2024 Short talks by Kathy McKeown and Robin Burke

Kathy's web page: https://www.cs.columbia.edu/~kathy/

Title: Addressing Large Language Models that Lie: Case Studies in Summarization

Kathleen McKeown Columbia University

The advent of large language models promises a new level of performance in generation of text of all kinds, enabling generation of text that is far more fluent, coherent and relevant than was previously possible. However, they also introduce a major new problem: they wholly hallucinate facts out of thin air. When summarizing an input document, they may incorrectly intermingle facts from the input, they may introduce facts that were not mentioned at all, and worse yet, they may even make up things that are not true in the real world. In this talk, I will discuss our work in characterizing the kinds of errors that can occur and methods that we have developed to help mitigate hallucination in language modeling approaches to text summarization for a variety of genres.

Kathleen R. McKeown is the Henry and Gertrude Rothschild Professor of Computer Science at Columbia University and the Founding Director of the Data Science Institute, serving as Director from 2012 to 2017. In earlier years, she served as Department Chair (1998-2003) and as Vice Dean for Research for the School of Engineering and Applied Science (2010-2012). A leading scholar and researcher in the field of natural language processing, McKeown focuses her research on the use of data for societal problems; her interests include text summarization, question answering, natural language generation, social media analysis and multilingual applications. She has received numerous honors and awards, including 2023 IEEE Innovation in Societal Infrastructure Award, American Philosophical Society Elected member, American Academy of Arts and Science elected member, American Association of Artificial Intelligence Fellow, a Founding Fellow of the Association for Computational Linguistics and an Association for Computing Machinery Fellow. Early on she received the National Science Foundation Presidential Young Investigator Award, and a National Science Foundation Faculty Award for Women. In 2010, she won both the Columbia Great Teacher Award—an honor bestowed by the students—and the Anita Borg Woman of Vision Award for Innovation.


Title: Multistakeholder fairness in recommender systems

Robin Burke University of Colorado Boulder

Abstract: Research in machine learning fairness makes two key simplifying assumptions that have proven challenging to move beyond. One assumption is that we can productively concentrate on a uni-dimensional version of the problem: achieving fairness for a single protected group defined by a single sensitive feature. The second assumption is that technical solutions need not engage with the essentially political nature of claims surrounding fairness. I argue that relaxing these assumptions is necessary for machine learning fairness to achieve practical utility. While some recent research in rich subgroup fairness has considered ways to relax the first assumption, these approaches require that fairness be defined in the same way for all groups, which amounts to a hardening of the second assumption. In this talk, I argue for a formulation of machine learning fairness based on social choice and exemplify the approach in the area of recommender systems. Social choice is inherently multi-agent, escaping the single group assumption and, in its classic formulation, places no constraints on agents' preferences. In addition, social choice was developed to formalize political decision-making mechanisms, such as elections, and therefore offers some hope of directly addressing the inherent politics of fairness. Social choice has complexities of its own, however, and the talk will outline a research agenda aimed at understanding the challenges and opportunities afforded by this approach to machine learning fairness.

Bio: Information Science Department Chair and Professor Robin Burke conducts research in personalized recommender systems, a field he helped found and develop. His most recent projects explore fairness, accountability and transparency in recommendation through the integration of objectives from diverse stakeholders. Professor Burke is the author of more than 150 peer-reviewed articles in various areas of artificial intelligence including recommender systems, machine learning and information retrieval. His work has received support from the National Science Foundation, the National Endowment for the Humanities, the Fulbright Commission and the MacArthur Foundation, among others.

03/06/2024 Jon Cai, CU Boulder Computer Science, PhD proposal defense

Title: Learning Fast and Slow with Semantics

Abstract: Abstract Meaning Representation(AMR) is a linguistic formalism that capture and encode semantics of natural language. It is one of the most widely accepted implementation over the truth value based theory of meanings. The impact of AMR has broadened since its introduction from its original design objective to help machine translation to more NLP tasks such as information extraction, summarizations and multi-modality semantic alignments etc. Meanwhile, AMR serves as a theoretical tool for computational semantics researches to advance semantic theories. Being able to model holistic semantics thus become one of the ultimate goal for NLP and computational linguistics community. Despite the amazing advancement of LLMs in recent years, we still see gaps between shallow and deep semantic understanding of machine learning models. In this proposal, we go through the generalization issues that AMR parsing models renders and our proposed solutions over how could we design new methodologies and analytical tools to help us navigate the labyrinth of modeling semantics via AMR.

03/13/2024 Veronica Qing Lyu,

Title:Faithful Chain of Thought Reasoning. ( https://aclanthology.org/2023.ijcnlp-main.20/ }

Abstract: While Chain-of-Thought (CoT) prompting boosts Language Models' (LM) performance on a gamut of complex reasoning tasks, the generated reasoning chain does not necessarily reflect how the model arrives at the answer (aka. faithfulness). We propose Faithful CoT, a reasoning framework involving two stages: Translation (Natural Language query → symbolic reasoning chain) and Problem Solving (reasoning chain → answer), using an LM and a deterministic solver respectively. This guarantees that the reasoning chain provides a faithful explanation of the final answer. Aside from interpretability, Faithful CoT also improves empirical performance: it outperforms standard CoT on 9 of 10 benchmarks from 4 diverse domains, with a relative accuracy gain of 6.3% on Math Word Problems (MWP), 3.4% on Planning, 5.5% on Multi-hop Question Answering (QA), and 21.4% on Relational Inference. Furthermore, with GPT-4 and Codex, it sets the new state-of-the-art few-shot performance on 7 datasets (with 95.0+ accuracy on 6 of them), showing a strong synergy between faithfulness and accuracy.

Bio: Veronica Qing Lyu is a fifth-year PhD student in Computer and Information Science at the University of Pennsylvania, advised by Chris Callison-Burch and Marianna Apidianaki. Her current research interests lie in the intersection of linguistics and natural language processing, explainable AI, and reasoning. Her paper "Faithful Chain-of-Thought Reasoning" received the Area Chair Award at IJCNLP-AACL 2023 (Interpretability and Analysis of Models for NLP track). She will co-organize a tutorial on “Explanations in the Era of Large Language Models” in NAACL 2024. Before Penn, she studied linguistics as an undergraduate student at the Department of Foreign Languages and Literatures at Tsinghua University.

03/20/2024 Jie Cao, CU Boulder/iSAT, practice talk

Title: Modularized Conversational Modeling for Efficient, Controllable, and Robust Real-World Applications

Abstract: Large Language Models~(LLM) make conversational AI accessible to everyone. Its general-purpose design benefits people across different domains, offering a powerful natural language interface to generate text, images, videos, and a broad range of AI services. However, a single monolithic black box is hard to maintain, scale, and control for all our communication goals, and it is often fragile and hallucinatory. To build robust conversational applications, such as high-stake healthcare and education domains, we must tackle various challenges carefully, e.g., hard-to-obtain data and annotations, controlling the model behaviors, etc. In this talk, I will discuss my research agenda on modularized conversational modeling, focusing on efficient modeling under minimal supervision and controllable modules via neurosymbolic interfaces. I will begin by introducing zero-shot dialogue state tracking via modeling the natural language descriptions of the functionalities of intent and slots and factorizing the tasks for supplementary pretraining. Next, I will describe managing uncertain controls via discrete latent variables for structured prediction and conditional generation tasks. Finally, I will demo a case study on educational AI agent design for a form of collaborative learning called Jigsaw Classroom by showing its challenges in data collection, analysis, evaluation, and deployment issues of noisy speech. I will end this talk by highlighting the future directions for better modularized conversational modeling and its applications.


03/27/2024 No Meeting - Spring Break
04/03/2024 CLASIC Industry Day
04/10/2024 iSAT Dry Run or other?
04/17/2024 Maggie Perkoff, dissertation proposal defense

Title: Bringing Everyone In: The Future of Collaboration with Conversational AI


04/24/2024 Téa Wright, Senior Thesis Defense

Title: PMM Adaptation for Lakota and Dakota with Noisy Data from OCR

Abstract: This research addresses the challenge of integrating low-resource languages, specifically Lakota and Dakota, into Natural Language Processing (NLP) technologies such as Pretrained Multilingual Models (PMMs). These languages are critically underrepresented in digital linguistic resources, worsening risks of linguistic erosion. Our study seeks to explore this problem by creating authentic, Optical Character Recognition (OCR)-derived datasets to examine the capabilities of PMMs in handling these underrepresented languages. We document and create annotated datasets for these languages to perform a basic evaluation of PMMs on word alignment under realistic, noisy data conditions. We investigate the zero-shot capabilities and analyze how variations in language and the presence of noise from handwriting or formatting in adaptation data affects performance. By contributing datasets for Lakota and Dakota as well as aiming to highlight strengths and weaknesses in existing NLP tools, we hope to promote more inclusive approaches in technological advancements.

05/01/2024 Sagi's Proposal
05/08/2024 Mary's Prelim


Past Schedules

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