Search Results for author:
Found 25 papers, 6 papers with code
X-Reflect: Cross-Reflection Prompting for Multimodal Recommendation
Large Language Models (LLMs) and Large Multimodal Models (LMMs) have been shown to enhance the effectiveness of enriching item descriptions, thereby improving the accuracy of recommendation systems.
Retrieval Augmentation via User Interest Clustering
In this paper, we address these challenges by introducing an intermediate "interest" layer between users and items.
Evolver: Chain-of-Evolution Prompting to Boost Large Multimodal Models for Hateful Meme Detection
To this end, we propose Evolver, which incorporates LMMs via Chain-of-Evolution (CoE) Prompting, by integrating the evolution attribute and in-context information of memes.
Representation Bias in Political Sample Simulations with Large Language Models
This study seeks to identify and quantify biases in simulating political samples with Large Language Models, specifically focusing on vote choice and public opinion.
INS-MMBench: A Comprehensive Benchmark for Evaluating LVLMs' Performance in Insurance
There is no systematic review of multimodal tasks in the insurance domain, nor a benchmark specifically designed to evaluate the capabilities of LVLMs in insurance.
Harnessing GPT-4V(ision) for Insurance: A Preliminary Exploration
The emergence of Large Multimodal Models (LMMs) marks a significant milestone in the development of artificial intelligence.
SoMeLVLM: A Large Vision Language Model for Social Media Processing
The powerful Large Vision Language Models make it possible to handle a variety of tasks simultaneously, but even with carefully designed prompting methods, the general domain models often fall short in aligning with the unique speaking style and context of social media tasks.
Human vs. LMMs: Exploring the Discrepancy in Emoji Interpretation and Usage in Digital Communication
Leveraging Large Multimodal Models (LMMs) to simulate human behaviors when processing multimodal information, especially in the context of social media, has garnered immense interest due to its broad potential and far-reaching implications.
CoCoT: Contrastive Chain-of-Thought Prompting for Large Multimodal Models with Multiple Image Inputs
When exploring the development of Artificial General Intelligence (AGI), a critical task for these models involves interpreting and processing information from multiple image inputs.
Ranked #3 on
Visual Reasoning
on Winoground
GPT-4V(ision) as A Social Media Analysis Engine
Our investigation begins with a preliminary quantitative analysis for each task using existing benchmark datasets, followed by a careful review of the results and a selection of qualitative samples that illustrate GPT-4V's potential in understanding multimodal social media content.
Mixture of Weak & Strong Experts on Graphs
We propose to decouple the two modalities by Mixture of weak and strong experts (Mowst), where the weak expert is a light-weight Multi-layer Perceptron (MLP), and the strong expert is an off-the-shelf GNN.
Understanding Divergent Framing of the Supreme Court Controversies: Social Media vs. News Outlets
Understanding the framing of political issues is of paramount importance as it significantly shapes how individuals perceive, interpret, and engage with these matters.
LLM-Rec: Personalized Recommendation via Prompting Large Language Models
Notably, the success of LLM-Rec lies in its prompting strategies, which effectively tap into the language model's comprehension of both general and specific item characteristics.
Learning to Evaluate the Artness of AI-generated Images
Assessing the artness of AI-generated images continues to be a challenge within the realm of image generation.
Bias or Diversity? Unraveling Fine-Grained Thematic Discrepancy in U.S. News Headlines
There is a broad consensus that news media outlets incorporate ideological biases in their news articles.
Predicting Adverse Neonatal Outcomes for Preterm Neonates with Multi-Task Learning
In this work, we first analyze the correlations between three adverse neonatal outcomes and then formulate the diagnosis of multiple neonatal outcomes as a multi-task learning (MTL) problem.
Human Behavior in the Time of COVID-19: Learning from Big Data
On one hand, human behavior is found to shape the spread of the disease.
Computational Assessment of Hyperpartisanship in News Titles
We first adopt a human-guided machine learning framework to develop a new dataset for hyperpartisan news title detection with 2, 200 manually labeled and 1. 8 million machine-labeled titles that were posted from 2014 to the present by nine representative media organizations across three media bias groups - Left, Central, and Right in an active learning manner.
Holistic Visual-Textual Sentiment Analysis with Prior Models
Visual-textual sentiment analysis aims to predict sentiment with the input of a pair of image and text, which poses a challenge in learning effective features for diverse input images.
Causal Inference via Nonlinear Variable Decorrelation for Healthcare Applications
Causal inference and model interpretability research are gaining increasing attention, especially in the domains of healthcare and bioinformatics.
Learning to Aggregate and Refine Noisy Labels for Visual Sentiment Analysis
The learned prototypes and their labels can be regarded as denoising features and labels for the local regions and can guide the training process to prevent the model from overfitting the noisy cases.
From Static to Dynamic Prediction: Wildfire Risk Assessment Based on Multiple Environmental Factors
Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States.
Social Media Study of Public Opinions on Potential COVID-19 Vaccines: Informing Dissent, Disparities, and Dissemination
People who have the worst personal pandemic experience are more likely to hold the anti-vaccine opinion.
Social and Information Networks
Monitoring Depression Trend on Twitter during the COVID-19 Pandemic
The COVID-19 pandemic has severely affected people's daily lives and caused tremendous economic loss worldwide.
In the Eyes of the Beholder: Analyzing Social Media Use of Neutral and Controversial Terms for COVID-19
To model the substantive difference of tweets with controversial terms and those with non-controversial terms, we apply topic modeling and LIWC-based sentiment analysis.
