4 citations · 7 across the 6 of their papers we have counts for
9 papers · 1 filter
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…
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…
ALISON: Fast and Effective Stylometric Authorship Obfuscation
Eric Xing, Saranya Venkatraman, Thai Le +1
Authorship Attribution (AA) and Authorship Obfuscation (AO) are two competing tasks of increasing importance in privacy research. Modern AA leverages an author's consistent writing…
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…