15 citations · 46 across the 27 of their papers we have counts for
32 papers · 1 filter
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…
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…