23 citations · 59 across the 18 of their papers we have counts for
19 papers
DIA-HARM: Dialectal Disparities in Harmful Content Detection Across 50 English Dialects
Jason Lucas, Matt Murtagh, Ali Al-Lawati +3
Harmful content detectors, particularly disinformation classifiers, are predominantly developed and evaluated on Standard American English (SAE), leaving their robustness to dialec…
BLUFF: Benchmarking the Detection of False and Synthetic Content across 58 Low-Resource Languages
Jason Lucas, Matt Murtagh-White, Adaku Uchendu +6
Multilingual falsehoods threaten information integrity worldwide, yet detection benchmarks remain confined to English or a few high-resource languages, leaving low-resource linguis…
Signature vs. Substance: Evaluating the Balance of Adversarial Resistance and Linguistic Quality in Watermarking Large Language Models
William Guo, Adaku Uchendu, Ana Smith
To mitigate the potential harms of Large Language Models (LLMs)generated text, researchers have proposed watermarking, a process of embedding detectable signals within text. With w…
Masks and Mimicry: Strategic Obfuscation and Impersonation Attacks on Authorship Verification
Kenneth Alperin, Rohan Leekha, Adaku Uchendu +5
The increasing use of Artificial Intelligence (AI) technologies, such as Large Language Models (LLMs) has led to nontrivial improvements in various tasks, including accurate author…
Beyond speculation: Measuring the growing presence of LLM-generated texts in multilingual disinformation
Dominik Macko, Aashish Anantha Ramakrishnan, Jason Samuel Lucas +4
Increased sophistication of large language models (LLMs) and the consequent quality of generated multilingual text raises concerns about potential disinformation misuse. While huma…
Topological Data Analysis Applications in Natural Language Processing: A Survey
Adaku Uchendu, Thai Le
The surge of data available on the Internet has driven the adoption of a wide range of computational methods for analyzing and extracting insights from large-scale data. Among thes…
Beemo: Benchmark of Expert-edited Machine-generated Outputs
Ekaterina Artemova, Jason Lucas, Saranya Venkatraman +4
The rapid proliferation of large language models (LLMs) has increased the volume of machine-generated texts (MGTs) and blurred text authorship in various domains. However, most exi…
PlagBench: Exploring the Duality of Large Language Models in Plagiarism Generation and Detection
Jooyoung Lee, Toshini Agrawal, Adaku Uchendu +3
Recent studies have raised concerns about the potential threats large language models (LLMs) pose to academic integrity and copyright protection. Yet, their investigation is predom…
Authorship Obfuscation in Multilingual Machine-Generated Text Detection
Dominik Macko, Robert Moro, Adaku Uchendu +7
High-quality text generation capability of recent Large Language Models (LLMs) causes concerns about their misuse (e.g., in massive generation/spread of disinformation). Machine-ge…
A Ship of Theseus: Curious Cases of Paraphrasing in LLM-Generated Texts
Nafis Irtiza Tripto, Saranya Venkatraman, Dominik Macko +5
In the realm of text manipulation and linguistic transformation, the question of authorship has been a subject of fascination and philosophical inquiry. Much like the Ship of These…
HANSEN: Human and AI Spoken Text Benchmark for Authorship Analysis
Nafis Irtiza Tripto, Adaku Uchendu, Thai Le +3
Authorship Analysis, also known as stylometry, has been an essential aspect of Natural Language Processing (NLP) for a long time. Likewise, the recent advancement of Large Language…
Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation
Jason Lucas, Adaku Uchendu, Michiharu Yamashita +3
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…
GPT-who: An Information Density-based Machine-Generated Text Detector
Saranya Venkatraman, Adaku Uchendu, Dongwon Lee
The Uniform Information Density (UID) principle posits that humans prefer to spread information evenly during language production. We examine if this UID principle can help capture…
TOPFORMER: Topology-Aware Authorship Attribution of Deepfake Texts with Diverse Writing Styles
Adaku Uchendu, Thai Le, Dongwon Lee
Recent advances in Large Language Models (LLMs) have enabled the generation of open-ended high-quality texts, that are non-trivial to distinguish from human-written texts. We refer…
Social network modeling and applications, a tutorial
Lisette Espín-Noboa, Tiago Peixoto, Fariba Karimi
Social networks have been widely studied over the last century from multiple disciplines to understand societal issues such as inequality in employment rates, managerial performanc…
Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?
Adaku Uchendu, Jooyoung Lee, Hua Shen +3
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…
Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective
Adaku Uchendu, Thai Le, Dongwon Lee
Two interlocking research questions of growing interest and importance in privacy research are Authorship Attribution (AA) and Authorship Obfuscation (AO). Given an artifact, espec…
Robustness of Bayesian Neural Networks to White-Box Adversarial Attacks
Adaku Uchendu, Daniel Campoy, Christopher Menart +1
Bayesian Neural Networks (BNNs), unlike Traditional Neural Networks (TNNs) are robust and adept at handling adversarial attacks by incorporating randomness. This randomness improve…
TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation
Adaku Uchendu, Zeyu Ma, Thai Le +2
Recent progress in generative language models has enabled machines to generate astonishingly realistic texts. While there are many legitimate applications of such models, there is…