62 citations · 130 across the 40 of their papers we have counts for
43 papers
Scaled Companion Linearizations for Coefficientwise Backward Stability
Javier Pérez
A central goal in solving a polynomial eigenvalue problem is to compute eigenpairs that are exact for a nearby polynomial, with each coefficient perturbation bounded by a modest mu…
Captioning Visualizations with Large Language Models (CVLLM): A Tutorial
Giuseppe Carenini, Jordon Johnson, Ali Salamatian
Automatically captioning visualizations is not new, but recent advances in large language models(LLMs) open exciting new possibilities. In this tutorial, after providing a brief re…
DeTriever: Decoder-representation-based Retriever for Improving NL2SQL In-Context Learning
Yuxi Feng, Raymond Li, Zhenan Fan +4
While in-context Learning (ICL) has proven to be an effective technique to improve the performance of Large Language Models (LLMs) in a variety of complex tasks, notably in transla…
Multi-Modal Video Topic Segmentation with Dual-Contrastive Domain Adaptation
Linzi Xing, Quan Tran, Fabian Caba +5
Video topic segmentation unveils the coarse-grained semantic structure underlying videos and is essential for other video understanding tasks. Given the recent surge in multi-modal…
Tracing Influence at Scale: A Contrastive Learning Approach to Linking Public Comments and Regulator Responses
Linzi Xing, Brad Hackinen, Giuseppe Carenini
U.S. Federal Regulators receive over one million comment letters each year from businesses, interest groups, and members of the public, all advocating for changes to proposed regul…
Visual Analytics for Generative Transformer Models
Raymond Li, Ruixin Yang, Wen Xiao +3
While transformer-based models have achieved state-of-the-art results in a variety of classification and generation tasks, their black-box nature makes them challenging for interpr…
Mixture-of-Linguistic-Experts Adapters for Improving and Interpreting Pre-trained Language Models
Raymond Li, Gabriel Murray, Giuseppe Carenini
In this work, we propose a method that combines two popular research areas by injecting linguistic structures into pre-trained language models in the parameter-efficient fine-tunin…
Diversity-Aware Coherence Loss for Improving Neural Topic Models
Raymond Li, Felipe González-Pizarro, Linzi Xing +2
The standard approach for neural topic modeling uses a variational autoencoder (VAE) framework that jointly minimizes the KL divergence between the estimated posterior and prior, i…
Personalized Abstractive Summarization by Tri-agent Generation Pipeline
Wen Xiao, Yujia Xie, Giuseppe Carenini +1
Tailoring outputs from large language models, like ChatGPT, to implicit user preferences remains a challenge despite their impressive generative capabilities. In this paper, we pro…
NL4Opt Competition: Formulating Optimization Problems Based on Their Natural Language Descriptions
Rindranirina Ramamonjison, Timothy T. Yu, Raymond Li +8
The Natural Language for Optimization (NL4Opt) Competition was created to investigate methods of extracting the meaning and formulation of an optimization problem based on its text…
Discourse Structure Extraction from Pre-Trained and Fine-Tuned Language Models in Dialogues
Chuyuan Li, Patrick Huber, Wen Xiao +3
Discourse processing suffers from data sparsity, especially for dialogues. As a result, we explore approaches to build discourse structures for dialogues, based on attention matric…
Attend to the Right Context: A Plug-and-Play Module for Content-Controllable Summarization
Wen Xiao, Lesly Miculicich, Yang Liu +2
Content-Controllable Summarization generates summaries focused on the given controlling signals. Due to the lack of large-scale training corpora for the task, we propose a plug-and…
Large Discourse Treebanks from Scalable Distant Supervision
Patrick Huber, Giuseppe Carenini
Discourse parsing is an essential upstream task in Natural Language Processing with strong implications for many real-world applications. Despite its widely recognized role, most r…
Towards Domain-Independent Supervised Discourse Parsing Through Gradient Boosting
Patrick Huber, Giuseppe Carenini
Discourse analysis and discourse parsing have shown great impact on many important problems in the field of Natural Language Processing (NLP). Given the direct impact of discourse…
Unsupervised Inference of Data-Driven Discourse Structures using a Tree Auto-Encoder
Patrick Huber, Giuseppe Carenini
With a growing need for robust and general discourse structures in many downstream tasks and real-world applications, the current lack of high-quality, high-quantity discourse tree…
Transition to Adulthood for Young People with Intellectual or Developmental Disabilities: Emotion Detection and Topic Modeling
Yan Liu, Maria Laricheva, Chiyu Zhang +5
Transition to Adulthood is an essential life stage for many families. The prior research has shown that young people with intellectual or development disabil-ities (IDD) have more…
Improving Topic Segmentation by Injecting Discourse Dependencies
Linzi Xing, Patrick Huber, Giuseppe Carenini
Recent neural supervised topic segmentation models achieve distinguished superior effectiveness over unsupervised methods, with the availability of large-scale training corpora sam…
Entity-based SpanCopy for Abstractive Summarization to Improve the Factual Consistency
Wen Xiao, Giuseppe Carenini
Despite the success of recent abstractive summarizers on automatic evaluation metrics, the generated summaries still present factual inconsistencies with the source document. In th…
Automated Utterance Labeling of Conversations Using Natural Language Processing
Maria Laricheva, Chiyu Zhang, Yan Liu +4
Conversational data is essential in psychology because it can help researchers understand individuals cognitive processes, emotions, and behaviors. Utterance labelling is a common…
Towards Understanding Large-Scale Discourse Structures in Pre-Trained and Fine-Tuned Language Models
Patrick Huber, Giuseppe Carenini
With a growing number of BERTology work analyzing different components of pre-trained language models, we extend this line of research through an in-depth analysis of discourse inf…
Predicting Above-Sentence Discourse Structure using Distant Supervision from Topic Segmentation
Patrick Huber, Linzi Xing, Giuseppe Carenini
RST-style discourse parsing plays a vital role in many NLP tasks, revealing the underlying semantic/pragmatic structure of potentially complex and diverse documents. Despite its im…
Human Guided Exploitation of Interpretable Attention Patterns in Summarization and Topic Segmentation
Raymond Li, Wen Xiao, Linzi Xing +3
The multi-head self-attention mechanism of the transformer model has been thoroughly investigated recently. In one vein of study, researchers are interested in understanding why an…
PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization
Wen Xiao, Iz Beltagy, Giuseppe Carenini +1
We introduce PRIMERA, a pre-trained model for multi-document representation with a focus on summarization that reduces the need for dataset-specific architectures and large amounts…
T3-Vis: a visual analytic framework for Training and fine-Tuning Transformers in NLP
Raymond Li, Wen Xiao, Lanjun Wang +2
Transformers are the dominant architecture in NLP, but their training and fine-tuning is still very challenging. In this paper, we present the design and implementation of a visual…