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Found 12 papers, 2 papers with code
Windows Agent Arena: Evaluating Multi-Modal OS Agents at Scale
To demonstrate Windows Agent Arena's capabilities, we also introduce a new multi-modal agent, Navi.
Can LLMs be Fooled? Investigating Vulnerabilities in LLMs
The advent of Large Language Models (LLMs) has garnered significant popularity and wielded immense power across various domains within Natural Language Processing (NLP).
Data Generation Using Large Language Models for Text Classification: An Empirical Case Study
Using Large Language Models (LLMs) to generate synthetic data for model training has become increasingly popular in recent years.
Securing Large Language Models: Threats, Vulnerabilities and Responsible Practices
Large language models (LLMs) have significantly transformed the landscape of Natural Language Processing (NLP).
Decoding the AI Pen: Techniques and Challenges in Detecting AI-Generated Text
Large Language Models (LLMs) have revolutionized the field of Natural Language Generation (NLG) by demonstrating an impressive ability to generate human-like text.
Extracting Self-Consistent Causal Insights from Users Feedback with LLMs and In-context Learning
Microsoft Windows Feedback Hub is designed to receive customer feedback on a wide variety of subjects including critical topics such as power and battery.
Multi-modal Misinformation Detection: Approaches, Challenges and Opportunities
As social media platforms are evolving from text-based forums into multi-modal environments, the nature of misinformation in social media is also transforming accordingly.
Deepfake Representation with Multilinear Regression
Generative neural network architectures such as GANs, may be used to generate synthetic instances to compensate for the lack of real data.
KNH: Multi-View Modeling with K-Nearest Hyperplanes Graph for Misinformation Detection
In this work, we introduce a novel generalization of graphs i. e., K-Nearest Hyperplanes graph (KNH) where the nodes are defined by higher order Euclidean subspaces for multi-view modeling of the nodes.
Identifying Misinformation from Website Screenshots
To capture this overall look, we take screenshots of news articles served by either misinformative or trustworthy web domains and leverage a tensor decomposition based semi-supervised classification technique.
Semi-Supervised Multi-aspect Detection of Misinformation using Hierarchical Joint Decomposition
Distinguishing between misinformation and real information is one of the most challenging problems in today's interconnected world.
Semi-supervised Content-based Detection of Misinformation via Tensor Embeddings
Most existing works on this topic focus on manual feature extraction and supervised classification models leveraging a large number of labeled (fake or real) articles.
