no code implementations • EMNLP 2020 • Adaku Uchendu, Thai Le, Kai Shu, Dongwon Lee
In recent years, the task of generating realistic short and long texts have made tremendous advancements.
no code implementations • 15 Jan 2024 • Dominik Macko, Robert Moro, Adaku Uchendu, Ivan Srba, Jason Samuel Lucas, Michiharu Yamashita, Nafis Irtiza Tripto, Dongwon Lee, Jakub Simko, Maria Bielikova
However, it is susceptible to authorship obfuscation (AO) methods, such as paraphrasing, which can cause MGTs to evade detection.
no code implementations • 14 Nov 2023 • Nafis Irtiza Tripto, Saranya Venkatraman, Dominik Macko, Robert Moro, Ivan Srba, Adaku Uchendu, Thai Le, Dongwon Lee
In the realm of text manipulation and linguistic transformation, the question of authorship has always been a subject of fascination and philosophical inquiry.
no code implementations • 25 Oct 2023 • Nafis Irtiza Tripto, Adaku Uchendu, Thai Le, Mattia Setzu, Fosca Giannotti, Dongwon Lee
Thus, we introduce the largest benchmark for spoken texts - HANSEN (Human ANd ai Spoken tExt beNchmark).
1 code implementation • 24 Oct 2023 • Jason Lucas, Adaku Uchendu, Michiharu Yamashita, Jooyoung Lee, Shaurya Rohatgi, Dongwon Lee
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).
1 code implementation • 20 Oct 2023 • Dominik Macko, Robert Moro, Adaku Uchendu, Jason Samuel Lucas, Michiharu Yamashita, Matúš Pikuliak, Ivan Srba, Thai Le, Dongwon Lee, Jakub Simko, Maria Bielikova
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.
1 code implementation • 9 Oct 2023 • Saranya Venkatraman, Adaku Uchendu, Dongwon Lee
We examine if this UID principle can help capture differences between Large Language Models (LLMs)-generated and human-generated texts.
no code implementations • 22 Sep 2023 • Adaku Uchendu, Thai Le, Dongwon Lee
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.
2 code implementations • 3 Apr 2023 • Adaku Uchendu, Jooyoung Lee, Hua Shen, Thai Le, Ting-Hao 'Kenneth' Huang, Dongwon Lee
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.
no code implementations • 19 Oct 2022 • 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).
no code implementations • 16 Nov 2021 • Adaku Uchendu, Daniel Campoy, Christopher Menart, Alexandra Hildenbrandt
Bayesian Neural Networks (BNNs), unlike Traditional Neural Networks (TNNs) are robust and adept at handling adversarial attacks by incorporating randomness.
3 code implementations • Findings (EMNLP) 2021 • Adaku Uchendu, Zeyu Ma, Thai Le, Rui Zhang, Dongwon Lee
Recent progress in generative language models has enabled machines to generate astonishingly realistic texts.