Researchers from MBZUAI have proposed a new taxonomy of eight temporal frames and studied their persuasive use in news discourse. They created a multilingual dataset by expertly annotating 458 English and German news articles, identifying over 2,000 temporally framed sentences and approximately 3,000 annotations. Their experiments demonstrated that temporal framing is learnable at the sentence level, with supervised models significantly outperforming zero-shot classification approaches. Why it matters: This research provides a valuable dataset and methodology for understanding how time-related language shapes interpretation in news, contributing to advancements in NLP for media analysis and potentially countering disinformation.
MBZUAI faculty and students will present 44 papers at the Empirical Methods in Natural Language Processing (EMNLP) conference in Singapore. Research topics include disinformation detection, social media analysis, dialogue generation, and Arabic LLMs. Preslav Nakov, Iryna Gurevych, Timothy Baldwin, Alham Fikri Aji, and Muhammad Abdul-Mageed are among the MBZUAI researchers presenting at the conference. Why it matters: MBZUAI's strong presence at a top NLP conference highlights the UAE's growing contributions to cutting-edge AI research and its increasing global prominence in the field.
The New Lines Institute published a report analyzing the risks associated with advanced AI systems. It examines potential harms like disinformation, bias, and autonomous weapons. Why it matters: The report highlights the need for proactive safety measures and ethical guidelines in AI development to mitigate negative impacts in the Middle East and globally.
The UAE government has issued a warning to the public regarding the dangers of misleading AI-generated videos, particularly those used to spread rumors and false information. Authorities emphasized the importance of verifying the credibility of video content before sharing it on social media. The warning highlights potential legal consequences for individuals involved in creating or disseminating such content. Why it matters: This proactive stance reflects growing concerns in the UAE about the misuse of AI-driven technologies and its commitment to combatting disinformation.
This paper describes the Nexus team's participation in the ArAIEval shared task focused on detecting propaganda and disinformation in Arabic. The team fine-tuned transformer models and experimented with zero- and few-shot learning using GPT-4. Nexus's system achieved 9th place in subtask 1A and 10th place in subtask 2A. Why it matters: The work contributes to the important goal of automatically identifying and mitigating the spread of disinformation in Arabic content, which is critical for maintaining societal trust and informed public discourse.
MBZUAI researchers developed a symbolic adversarial learning framework (SALF) for fake news detection using LLM-powered agents. SALF employs a generator and a detector in a debate-like setup, judged by another LLM, to improve the agents' ability to create and identify fake news. Testing showed that the SALF generator degraded the performance of existing fake news detectors by 53.4% on Chinese and 34.2% on English datasets. Why it matters: This research offers a novel approach to combating the evolving threat of LLM-generated disinformation, a critical issue for maintaining reliable information ecosystems in the region and globally.
Muhammad Arslan Manzoor became MBZUAI's first NLP Ph.D. graduate, focusing his research on media bias under Professor Preslav Nakov. His thesis, 'MGM,' explored using audience overlap graphs to predict the factuality and bias of news media, an approach that differs from traditional textual analysis. Manzoor's work aims to improve the efficiency of media profiling in real-time by leveraging relationships captured in media graphs. Why it matters: This research offers innovative methods for identifying bias in news, which is crucial for promoting informed social discourse and combating disinformation in the region.
MBZUAI alumnus Zain Muhammed Mujahid is pursuing a Ph.D. at the University of Copenhagen, focusing on factual text generation in LLMs to combat misinformation. During his master's at MBZUAI, he researched political bias and misinformation in media outlets using LLMs, under the mentorship of Professor Preslav Nakov. His master's thesis involved assessing a media outlet's factual reporting level and political leaning on topics like immigration and economy. Why it matters: This research addresses a critical challenge in AI, enhancing the reliability of LLMs and mitigating the spread of disinformation, an issue of global concern.