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Found 9 papers, 0 papers with code
RiskLabs: Predicting Financial Risk Using Large Language Model Based on Multi-Sources Data
Through comparative experiments, we demonstrate how different data sources contribute to financial risk assessment and discuss the critical role of LLMs in this context.
Can Large Language Models Detect Misinformation in Scientific News Reporting?
The central research question of this paper is whether it is possible to use large language models (LLMs) to detect misinformation in scientific reporting.
Revisiting Attention Weights as Explanations from an Information Theoretic Perspective
Attention mechanisms have recently demonstrated impressive performance on a range of NLP tasks, and attention scores are often used as a proxy for model explainability.
MMCoVaR: Multimodal COVID-19 Vaccine Focused Data Repository for Fake News Detection and a Baseline Architecture for Classification
We also provide a novel architecture that classifies the news data into misinformation or truth to provide a baseline performance for this dataset.
Learning Models for Suicide Prediction from Social Media Posts
We propose a deep learning architecture and test three other machine learning models to automatically detect individuals that will attempt suicide within (1) 30 days and (2) six months, using their social media post data provided in the CLPsych 2021 shared task.
Causal-TGAN: Generating Tabular Data Using Causal Generative Adversarial Networks
Though there are prior works that have demonstrated great progress, most of them learn the correlations in the data distributions rather than the true processes in which the datasets are naturally generated.
Explainable Rumor Detection using Inter and Intra-feature Attention Networks
With social media becoming ubiquitous, information consumption from this media has also increased.
Explainable CNN-attention Networks (C-Attention Network) for Automated Detection of Alzheimer's Disease
In this work, we propose three explainable deep learning architectures to automatically detect patients with Alzheimer`s disease based on their language abilities.
Personalized Early Stage Alzheimer's Disease Detection: A Case Study of President Reagan's Speeches
In this paper, we show that machine learning-based unsupervised clustering of and anomaly detection with linguistic biomarkers are promising approaches for intuitive visualization and personalized early stage detection of Alzheimer`s disease.
