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Preslav Nakov

Professor @ Mohamed bin Zayed University of Artificial Intelligence

disinformation

multimodal

llm

memes

propaganda detection

fact-checking

multilingual

harmfulness

fake news

misinformation

dataset

persuasion techniques detection

persuasion techniques

benchmark

framing

62

presentations

31

number of views

SHORT BIO

Dr. Preslav Nakov is a Professor at Mohamed bin Zayed University of Artificial Intelligence. Previously, he was a Principal Scientist at the Qatar Computing Research Institute (QCRI), HBKU, where he led the Tanbih mega-project, developed in collaboration with MIT, which aims to limit the impact of "fake news", propaganda and media bias by making users aware of what they are reading, thus promoting media literacy and critical thinking. He received his PhD degree in Computer Science from the University of California at Berkeley, supported by a Fulbright grant. Dr. Preslav Nakov is President of ACL SIGLEX, Secretary of ACL SIGSLAV, Secretary of the Truth and Trust Online board of trustees, PC chair of ACL 2022, and a member of the EACL advisory board. He is also member of the editorial board of several journals including Computational Linguistics, TACL, ACM TOIS, IEEE TASL, IEEE TAC, CS&L, NLE, AI Communications, and Frontiers in AI. He authored a Morgan & Claypool book on Semantic Relations between Nominals, two books on computer algorithms, and 250+ research papers. He received a Best Paper Award at ACM WebSci'2022, a Best Long Paper Award at CIKM'2020, a Best Demo Paper Award (Honorable Mention) at ACL'2020, a Best Task Paper Award (Honorable Mention) at SemEval'2020, a Best Poster Award at SocInfo'2019, and the Young Researcher Award at RANLP’2011. He was also the first to receive the Bulgarian President's John Atanasoff award, named after the inventor of the first automatic electronic digital computer. Dr. Nakov's research was featured by over 100 news outlets, including Forbes, Boston Globe, Aljazeera, DefenseOne, Business Insider, MIT Technology Review, Science Daily, Popular Science, Fast Company, The Register, WIRED, and Engadget, among others.

Presentations

Can Machines Resonate with Humans? Evaluating the Emotional and Empathic Comprehension of LMs

Muhammad Arslan Manzoor and 3 other authors

Recent Advances in Online Hate Speech Moderation: Multimodality and the Role of Large Models

Ming Shan Hee and 6 other authors

OpenFactCheck: A Unified Framework for Factuality Evaluation of LLMs

Hasan Iqbal and 6 other authors

DocEditAgent: Document Structure Editing Via Multimodal LLM Grounding

Manan Suri and 7 other authors

SAFARI: Cross-lingual Bias and Factuality Detection in News Media and News Articles

Dilshod Azizov and 4 other authors

Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers

Yuxia Wang and 12 other authors

Factuality of Large Language Models in the Year 2024

Yuxia Wang and 6 other authors

MemeMQA: Multimodal Question Answering for Memes via Rationale-Based Inferencing

Siddhant Agarwal and 3 other authors

Missci: Reconstructing Fallacies in Misrepresented Science

Max Glockner and 3 other authors

M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection

Yuxia Wang and 13 other authors

EXAMS-V: A Multi-Discipline Multilingual Multimodal Exam Benchmark for Evaluating Vision Language Models

Rocktim Das and 5 other authors

A Chinese Dataset for Evaluating the Safeguards in Large Language Models

Yuxia Wang and 8 other authors

ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic

Fajri Koto and 12 other authors

Fact-Checking the Output of Large Language Models via Token-Level Uncertainty Quantification

Ekaterina Fadeeva and 11 other authors

Large Language Models are Few-Shot Training Example Generators: A Case Study in Fallacy Recognition

Tariq Alhindi and 2 other authors

A Survey on Predicting the Factuality and the Bias of News Media

Preslav Nakov and 5 other authors

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