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Found 9 papers, 4 papers with code
FANG-COVID: A New Large-Scale Benchmark Dataset for Fake News Detection in German
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.
Membership Inference Attacks against Language Models via Neighbourhood Comparison
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.
Smaller Language Models are Better Black-box Machine-Generated Text Detectors
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.
Psychologically-Inspired Causal Prompts
In this work, we take sentiment classification as an example and look into the causal relations between the review (X) and sentiment (Y).
Unique Identification of 50,000+ Virtual Reality Users from Head & Hand Motion Data
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.
Understanding Stereotypes in Language Models: Towards Robust Measurement and Zero-Shot Debiasing
Generated texts from large pretrained language models have been shown to exhibit a variety of harmful, human-like biases about various demographics.
Differentially Private Language Models for Secure Data Sharing
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.
The Limits of Word Level Differential Privacy
As the issues of privacy and trust are receiving increasing attention within the research community, various attempts have been made to anonymize textual data.
Measuring the Impact of (Psycho-)Linguistic and Readability Features and Their Spill Over Effects on the Prediction of Eye Movement Patterns
There is a growing interest in the combined use of NLP and machine learning methods to predict gaze patterns during naturalistic reading.
