Preslav Nakov is Professor and Department Chair for NLP at the Mohamed bin Zayed University of Artificial Intelligence. He is part of the core team that developed Jais, the world's best open-source Arabic-centric LLM, as well as part of the LLM360 team at MBZUAI. 

Previously, he was Principal Scientist at the Qatar Computing Research Institute, 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. He is Chair of the European Chapter of the Association for Computational Linguistics (EACL), Secretary of ACL SIGSLAV, and Secretary of the Truth and Trust Online board of trustees. Formerly, he was PC chair of ACL 2022, and President of ACL SIGLEX. 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 Resource Paper Award at EACL'2024, 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. His research was featured by over 100 news outlets, including Reuters, Forbes, Financial Times, CNN, Boston Globe, Aljazeera, DefenseOne, Business Insider, MIT Technology Review, Science Daily, Popular Science, Fast Company, The Register, WIRED, and Engadget, among others.

Talk Title: Factuality Challenges in the Era of Large Language Models

Abstract: Preslav discusses the risks, the challenges, and the opportunities that Large Language Models (LLMs) bring regarding factuality. He then presents some recent work on using LLMs for fact-checking, on detecting machine-generated text, and on fighting the ongoing misinformation pollution with LLMs. Finally, He presents a number of LLM fact-checking tools recently developed at MBZUAI: (i) LM-Polygraph, a tool to predict an LLM's uncertainty in its output using cheap and fast uncertainty quantification techniques, (ii) Factcheck-Bench, a fine-grained evaluation benchmark and framework for fact-checking the output of LLMs, (iii) Loki, an open-source tool for fact-checking the output of LLMs, developed based on Factcheck-Bench and optimized for speed and quality, (iv) OpenFactCheck, a framework for fact-checking LLM output, for building customized fact-checking systems, and for benchmarking LLMs for factuality, (v) LLM-DetectAIve, a tool for machine-generated text detection, and (vi) FRAPPE, a FRAming, Persuasion, and Propaganda Explorer.

Julia Mendelsohn is an Assistant Professor at the University of Maryland College of Information. She previously served as a postdoctoral scholar at the University of Chicago Data Science Institute and earned her PhD in Information from the University of Michigan School of Information. She also holds a BA in Linguistics and an MS in Computer Science from Stanford University. 

Her research focuses on the intersection of language, politics, and computation. Drawing on interdisciplinary approaches such as natural language processing, political communication, sociolinguistics, and psychology, she studies subtle rhetorical dynamics in online political discourse and their broader societal implications. Her work includes analyzing the framing of sociopolitical issues in media, investigating harmful and implicit language such as dehumanization and dogwhistles (with a recent focus on antisemitism), and advancing computational sociolinguistics to better understand the relationship between language use and social networks.

Talk Title: Social Meaning, Information Disorder, and the LLM of it all.

Abstract: Information disorder is often studied as a problem of veracity, but many forms of manipulation rely on socially-situated interpretation: how communicators draw on shared assumptions, group identities, and cultural associations to shape audiences’ understandings of problems, causes, and solutions. This talk centers on the dual role of LLMs in studying how information disorder operates through social meaning. On one hand, LLMs offer powerful tools for analyzing linguistic strategies that shape interpretation, including metaphorical framing, dehumanization, and dogwhistle communication. On the other, they introduce risks as research tools: their outputs can shift based on prompt design and biased framing. As communicative systems, LLMs may also contribute to information disorder when steered toward deception in interactive settings. Although LLMs expand how researchers can study information disorder at scale, they also raise broader questions about when they can be trusted as instruments, interlocutors, or sources of information.