1 code implementation • EMNLP (FEVER) 2021 • Justus Mattern, Yu Qiao, Elma Kerz, Daniel Wiechmann, Markus Strohmaier
As the world continues to fight the COVID-19 pandemic, it is simultaneously fighting an ‘infodemic’ – a flood of disinformation and spread of conspiracy theories leading to health threats and the division of society.
1 code implementation • 29 May 2023 • Justus Mattern, FatemehSadat Mireshghallah, Zhijing Jin, Bernhard Schölkopf, Mrinmaya Sachan, Taylor Berg-Kirkpatrick
To investigate whether this fragility provides a layer of safety, we propose and evaluate neighbourhood attacks, which compare model scores for a given sample to scores of synthetically generated neighbour texts and therefore eliminate the need for access to the training data distribution.
no code implementations • 17 May 2023 • Niloofar Mireshghallah, Justus Mattern, Sicun Gao, Reza Shokri, Taylor Berg-Kirkpatrick
With the advent of fluent generative language models that can produce convincing utterances very similar to those written by humans, distinguishing whether a piece of text is machine-generated or human-written becomes more challenging and more important, as such models could be used to spread misinformation, fake news, fake reviews and to mimic certain authors and figures.
1 code implementation • 2 May 2023 • Zhiheng Lyu, Zhijing Jin, Justus Mattern, Rada Mihalcea, Mrinmaya Sachan, Bernhard Schoelkopf
In this work, we take sentiment classification as an example and look into the causal relations between the review (X) and sentiment (Y).
1 code implementation • 17 Feb 2023 • Vivek Nair, Wenbo Guo, Justus Mattern, Rui Wang, James F. O'Brien, Louis Rosenberg, Dawn Song
With the recent explosive growth of interest and investment in virtual reality (VR) and the so-called "metaverse," public attention has rightly shifted toward the unique security and privacy threats that these platforms may pose.
no code implementations • 20 Dec 2022 • Justus Mattern, Zhijing Jin, Mrinmaya Sachan, Rada Mihalcea, Bernhard Schölkopf
Generated texts from large pretrained language models have been shown to exhibit a variety of harmful, human-like biases about various demographics.
no code implementations • 25 Oct 2022 • Justus Mattern, Zhijing Jin, Benjamin Weggenmann, Bernhard Schoelkopf, Mrinmaya Sachan
To protect the privacy of individuals whose data is being shared, it is of high importance to develop methods allowing researchers and companies to release textual data while providing formal privacy guarantees to its originators.
no code implementations • Findings (NAACL) 2022 • Justus Mattern, Benjamin Weggenmann, Florian Kerschbaum
As the issues of privacy and trust are receiving increasing attention within the research community, various attempts have been made to anonymize textual data.
no code implementations • ACL 2022 • Daniel Wiechmann, Yu Qiao, Elma Kerz, Justus Mattern
There is a growing interest in the combined use of NLP and machine learning methods to predict gaze patterns during naturalistic reading.