Search Results for author:
Found 38 papers, 19 papers with code
Detecting False Claims in Low-Resource Regions: A Case Study of Caribbean Islands
The COVID-19 pandemic has created threats to global health control.
Authorship Attribution for Neural Text Generation
In recent years, the task of generating realistic short and long texts have made tremendous advancements.
ANCHOR: LLM-driven News Subject Conditioning for Text-to-Image Synthesis
With Large Language Models (LLM) achieving success in language and commonsense reasoning tasks, we explore the ability of different LLMs to identify and understand key subjects from abstractive captions.
Fakes of Varying Shades: How Warning Affects Human Perception and Engagement Regarding LLM Hallucinations
This research aims to understand the human perception of LLM hallucinations by systematically varying the degree of hallucination (genuine, minor hallucination, major hallucination) and examining its interaction with warning (i. e., a warning of potential inaccuracies: absent vs. present).
ALISON: Fast and Effective Stylometric Authorship Obfuscation
AO is the corresponding adversarial task, aiming to modify a text in such a way that its semantics are preserved, yet an AA model cannot correctly infer its authorship.
Authorship Obfuscation in Multilingual Machine-Generated Text Detection
However, it is susceptible to authorship obfuscation (AO) methods, such as paraphrasing, which can cause MGTs to evade detection.
A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts
In the realm of text manipulation and linguistic transformation, the question of authorship has always been a subject of fascination and philosophical inquiry.
HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis
Thus, we introduce the largest benchmark for spoken texts - HANSEN (Human ANd ai Spoken tExt beNchmark).
Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation
Recent ubiquity and disruptive impacts of large language models (LLMs) have raised concerns about their potential to be misused (. i. e, generating large-scale harmful and misleading content).
MULTITuDE: Large-Scale Multilingual Machine-Generated Text Detection Benchmark
There is a lack of research into capabilities of recent LLMs to generate convincing text in languages other than English and into performance of detectors of machine-generated text in multilingual settings.
GPT-who: An Information Density-based Machine-Generated Text Detector
We examine if this UID principle can help capture differences between Large Language Models (LLMs)-generated and human-generated texts.
TOPFORMER: Topology-Aware Authorship Attribution of Deepfake Texts with Diverse Writing Styles
We propose TopFormer to improve existing AA solutions by capturing more linguistic patterns in deepfake texts by including a Topological Data Analysis (TDA) layer in the Transformer-based model.
Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?
Advances in Large Language Models (e. g., GPT-4, LLaMA) have improved the generation of coherent sentences resembling human writing on a large scale, resulting in the creation of so-called deepfake texts.
NoisyHate: Benchmarking Content Moderation Machine Learning Models with Human-Written Perturbations Online
In this paper, we introduce a benchmark test set containing human-written perturbations online for toxic speech detection models.
Imputing Knowledge Tracing Data with Subject-Based Training via LSTM Variational Autoencoders Frameworks
The paper finally compare the model performance between training the original data and training the data imputed with generated data from non-subject based model VAE-NS and subject-based training models (i. e., VAE and LVAE).
CRYPTEXT: Database and Interactive Toolkit of Human-Written Text Perturbations in the Wild
CRYPTEXT is a data-intensive application that provides the users with a database and several tools to extract and interact with human-written perturbations.
ANNA: Abstractive Text-to-Image Synthesis with Filtered News Captions
Advancements in Text-to-Image synthesis over recent years have focused more on improving the quality of generated samples on datasets with descriptive captions.
Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective
Two interlocking research questions of growing interest and importance in privacy research are Authorship Attribution (AA) and Authorship Obfuscation (AO).
Perturbations in the Wild: Leveraging Human-Written Text Perturbations for Realistic Adversarial Attack and Defense
We find that adversarial texts generated by ANTHRO achieve the best trade-off between (1) attack success rate, (2) semantic preservation of the original text, and (3) stealthiness--i. e. indistinguishable from human writings hence harder to be flagged as suspicious.
Do Language Models Plagiarize?
Our results suggest that (1) three types of plagiarism widely exist in LMs beyond memorization, (2) both size and decoding methods of LMs are strongly associated with the degrees of plagiarism they exhibit, and (3) fine-tuned LMs' plagiarism patterns vary based on their corpus similarity and homogeneity.
JAMES: Normalizing Job Titles with Multi-Aspect Graph Embeddings and Reasoning
In online job marketplaces, it is important to establish a well-defined job title taxonomy for various downstream tasks (e. g., job recommendation, users' career analysis, and turnover prediction).
Socialbots on Fire: Modeling Adversarial Behaviors of Socialbots via Multi-Agent Hierarchical Reinforcement Learning
To this question, we successfully demonstrate that indeed it is possible for adversaries to exploit computational learning mechanism such as reinforcement learning (RL) to maximize the influence of socialbots while avoiding being detected.
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation
Recent progress in generative language models has enabled machines to generate astonishingly realistic texts.
MathBERT: A Pre-trained Language Model for General NLP Tasks in Mathematics Education
Due to the nature of mathematical texts, which often use domain specific vocabulary along with equations and math symbols, we posit that the development of a new BERT model for mathematics would be useful for many mathematical downstream tasks.
Large-Scale Data-Driven Airline Market Influence Maximization
On top of the prediction models, we define a budget-constrained flight frequency optimization problem to maximize the market influence over 2, 262 routes.
Classifying Math KCs via Task-Adaptive Pre-Trained BERT
Educational content labeled with proper knowledge components (KCs) are particularly useful to teachers or content organizers.
MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories
Automated metaphor detection is a challenging task to identify metaphorical expressions of words in a sentence.
Detecting micro fractures: A comprehensive comparison of conventional and machine-learning based segmentation methods
The segmentation results from all five methods are compared to each other in terms of segmentation quality and time efficiency.
A Sweet Rabbit Hole by DARCY: Using Honeypots to Detect Universal Trigger's Adversarial Attacks
The Universal Trigger (UniTrigger) is a recently-proposed powerful adversarial textual attack method.
SHIELD: Defending Textual Neural Networks against Multiple Black-Box Adversarial Attacks with Stochastic Multi-Expert Patcher
Even though several methods have proposed to defend textual neural network (NN) models against black-box adversarial attacks, they often defend against a specific text perturbation strategy and/or require re-training the models from scratch.
Achieving User-Side Fairness in Contextual Bandits
Personalized recommendation based on multi-arm bandit (MAB) algorithms has shown to lead to high utility and efficiency as it can dynamically adapt the recommendation strategy based on feedback.
MALCOM: Generating Malicious Comments to Attack Neural Fake News Detection Models
In recent years, the proliferation of so-called "fake news" has caused much disruptions in society and weakened the news ecosystem.
CoAID: COVID-19 Healthcare Misinformation Dataset
As the COVID-19 virus quickly spreads around the world, unfortunately, misinformation related to COVID-19 also gets created and spreads like wild fire.
Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements
In recent years, disinformation including fake news, has became a global phenomenon due to its explosive growth, particularly on social media.
GRACE: Generating Concise and Informative Contrastive Sample to Explain Neural Network Model's Prediction
Despite the recent development in the topic of explainable AI/ML for image and text data, the majority of current solutions are not suitable to explain the prediction of neural network models when the datasets are tabular and their features are in high-dimensional vectorized formats.
Deep Reinforcement Learning for Personalized Search Story Recommendation
In recent years, \emph{search story}, a combined display with other organic channels, has become a major source of user traffic on platforms such as e-commerce search platforms, news feed platforms and web and image search platforms.
FakeNewsNet: A Data Repository with News Content, Social Context and Dynamic Information for Studying Fake News on Social Media
However, fake news detection is a non-trivial task, which requires multi-source information such as news content, social context, and dynamic information.
Social and Information Networks
Regularizing Matrix Factorization with User and Item Embeddings for Recommendation
Following recent successes in exploiting both latent factor and word embedding models in recommendation, we propose a novel Regularized Multi-Embedding (RME) based recommendation model that simultaneously encapsulates the following ideas via decomposition: (1) which items a user likes, (2) which two users co-like the same items, (3) which two items users often co-liked, and (4) which two items users often co-disliked.
