1 code implementation • 1 Feb 2024 • Eric Xing, Saranya Venkatraman, Thai Le, Dongwon Lee
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.
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 • 18 Oct 2023 • Pranav Narayanan Venkit, Mukund Srinath, Sanjana Gautam, Saranya Venkatraman, Vipul Gupta, Rebecca J. Passonneau, Shomir Wilson
We conduct an inquiry into the sociotechnical aspects of sentiment analysis (SA) by critically examining 189 peer-reviewed papers on their applications, models, and datasets.
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 • 29 Mar 2023 • Saranya Venkatraman, He He, David Reitter
We find that (i) surprisingly, model-generated responses follow the UID principle to a greater extent than human responses, and (ii) decoding algorithms that promote UID do not generate higher-quality responses.