16 citations · 73 across the 29 of their papers we have counts for
6 papers · 1 filter
VQA-GEN: A Visual Question Answering Benchmark for Domain Generalization
Suraj Jyothi Unni, Raha Moraffah, Huan Liu
Visual question answering (VQA) models are designed to demonstrate visual-textual reasoning capabilities. However, their real-world applicability is hindered by a lack of comprehen…
How Reliable Are AI-Generated-Text Detectors? An Assessment Framework Using Evasive Soft Prompts
Tharindu Kumarage, Paras Sheth, Raha Moraffah +2
In recent years, there has been a rapid proliferation of AI-generated text, primarily driven by the release of powerful pre-trained language models (PLMs). To address the issue of…
ConDA: Contrastive Domain Adaptation for AI-generated Text Detection
Amrita Bhattacharjee, Tharindu Kumarage, Raha Moraffah +1
Large language models (LLMs) are increasingly being used for generating text in a variety of use cases, including journalistic news articles. Given the potential malicious nature i…
Towards LLM-guided Causal Explainability for Black-box Text Classifiers
Amrita Bhattacharjee, Raha Moraffah, Joshua Garland +1
With the advent of larger and more complex deep learning models, such as in Natural Language Processing (NLP), model qualities like explainability and interpretability, albeit high…
Causality Guided Disentanglement for Cross-Platform Hate Speech Detection
Paras Sheth, Tharindu Kumarage, Raha Moraffah +2
Social media platforms, despite their value in promoting open discourse, are often exploited to spread harmful content. Current deep learning and natural language processing models…
PEACE: Cross-Platform Hate Speech Detection- A Causality-guided Framework
Paras Sheth, Tharindu Kumarage, Raha Moraffah +2
Hate speech detection refers to the task of detecting hateful content that aims at denigrating an individual or a group based on their religion, gender, sexual orientation, or othe…