12 citations · 13 across the 8 of their papers we have counts for
8 papers
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…
Neural Multimodal Topic Modeling: A Comprehensive Evaluation
Felipe González-Pizarro, Giuseppe Carenini
Neural topic models can successfully find coherent and diverse topics in textual data. However, they are limited in dealing with multimodal datasets (e.g., images and text). This p…
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…
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…