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Found 5 papers, 3 papers with code
Lying Blindly: Bypassing ChatGPT's Safeguards to Generate Hard-to-Detect Disinformation Claims at Scale
As Large Language Models (LLMs) become more proficient, their misuse in large-scale viral disinformation campaigns is a growing concern.
Analysing State-Backed Propaganda Websites: a New Dataset and Linguistic Study
This paper analyses two hitherto unstudied sites sharing state-backed disinformation, Reliable Recent News (rrn. world) and WarOnFakes (waronfakes. com), which publish content in Arabic, Chinese, English, French, German, and Spanish.
Comparison between parameter-efficient techniques and full fine-tuning: A case study on multilingual news article classification
Adapters and Low-Rank Adaptation (LoRA) are parameter-efficient fine-tuning techniques designed to make the training of language models more efficient.
A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation
This motivated us to carry out a comparative study of the characteristics of COVID-19 misinformation versus those of accurate COVID-19 information through a large-scale computational analysis of over 242 million tweets.
SheffieldVeraAI at SemEval-2023 Task 3: Mono and multilingual approaches for news genre, topic and persuasion technique classification
For Subtask 2 (Framing), we achieved first place in 3 languages, and the best average rank across all the languages, by using two separate ensembles: a monolingual RoBERTa-MUPPETLARGE and an ensemble of XLM-RoBERTaLARGE with adapters and task adaptive pretraining.
