Leave-One-Out (LOO) scores provide estimates of feature importance in neural networks, for
adversarial attacks. In this work, we present context-free word scores as a query-efficient …

Warning: this paper contains content that maybe offensive or upsetting. Recent research in
Natural Language Processing (NLP) has advanced the development of various toxicity …

Modern large language models (LLMs), such as GPT-4 or ChatGPT, are capable of
producing fluent text in natural languages, making their output hard to manually differentiate …

Background: Deception detection through analysing language is a promising avenue using
both human judgments and automated machine learning judgments. For both forms of …

Adversarial examples in NLP are receiving increasing research attention. One line of
investigation is the generation of word-level adversarial examples against fine-tuned …

This dissertation proposes a framework of user-centered security in Natural Language
Processing (NLP), and demonstrates how it can improve the accessibility of related …

Natural language processing (NLP) systems are often used for adversarial tasks such as
detecting spam, abuse, hate speech, and fake news. Properly evaluating such systems …

Crowdsourcing has emerged as a collaborative method to accomplish various tasks using
open calls posted on platforms such as Amazon Mechanical Turk. Most requesters who post …

The growing use of data-driven decision systems based on Artificial Intelligence (AI) by
governments, companies and social organizations has given more attention to the …

Despite their success, machine learning models have been shown to be susceptible to
adversarial examples: carefully constructed perturbations of model inputs that are intended …