NYUAD and MBZUAI co-hosted the 2022 Conference on Empirical Methods in Natural Language Processing (EMNLP) in Abu Dhabi from December 7-11. EMNLP is a top-tier NLP and AI conference organized by the ACL special interest group on linguistic data (SIGDAT). MBZUAI's Natural Language Processing Department is actively developing NLP datasets and methods to solve social problems. Why it matters: Hosting EMNLP in the UAE highlights the growing importance of NLP research in the region and the increasing contributions of local institutions like MBZUAI to the field.
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.
Researchers from MBZUAI and Monash University presented a study at EMNLP 2024 examining LLMs' ability to interpret empathy, emotion, and morality in written stories. The study builds on a framework for modeling empathic similarity between narratives, using the EmpathicStories dataset. They are exploring ways to improve LLMs' capabilities with complex concepts like empathy, especially for applications in fields like healthcare. Why it matters: Enhancing LLMs with empathic understanding could lead to more effective and human-centered AI applications, particularly in sensitive domains requiring nuanced communication.
MBZUAI researchers presented new resources at EMNLP for improving the factuality of LLMs, including a web application for fact-checking LLM-generated text and benchmarks for evaluating automated fact-checkers. They found that current automated fact-checkers miss nearly 40% of false claims generated by LLMs. The study breaks down the fact-checking process into eight tasks, including decomposition and decontextualization, to identify where systems fail. Why it matters: This work addresses a critical challenge in the deployment of LLMs by providing tools and methods for improving their reliability and trustworthiness, which is essential for widespread adoption in sensitive applications.
A study co-authored by researchers from UC Berkeley, University of the Witwatersrand, Lelapa AI, and MBZUAI received the Outstanding Paper Award at EMNLP 2024. The paper critiques the term "low-resource" languages in NLP, highlighting its limitations in capturing the diverse challenges faced by different languages. The authors propose a more detailed analysis of resourcedness to encourage targeted support for languages currently underserved by technology. Why it matters: The research challenges assumptions in NLP and promotes more nuanced approaches to supporting the world's many languages, including Arabic, in AI systems.
MBZUAI researchers received high honors at EMNLP 2025 for two research papers, placing them in the top 2% of accepted work. One paper, MAviS, is a multimodal AI system that identifies bird species by combining images, sounds, and text. The other award-winning paper focuses on uncertainty in LLM-as-a-Judge. Why it matters: The recognition highlights MBZUAI's growing influence in NLP and multimodal AI research, particularly in domain-specific applications like biodiversity conservation.
Thamar Solorio of MBZUAI served as general chair of EMNLP 2024, which hosted over 4,000 attendees. MBZUAI researchers presented nearly 50 studies, including one co-authored by Solorio and Monojit Choudhury that received an Outstanding Paper Award. Key themes included cultural awareness, machine-generated content detection, and LLM empathy and cultural representation. Why it matters: MBZUAI's strong presence at EMNLP highlights its growing influence in the international NLP research community and its focus on culturally aware AI.
MBZUAI Professor Timothy Baldwin delivered the presidential keynote at the 60th Annual Meeting of the Association for Computational Linguistics (ACL). Baldwin also published three papers at the conference, including work on biomedical literature summarization, NLP for Indonesian languages, and understanding procedural texts. The papers address challenges such as reducing human effort in reviewing medical documents and digitally preserving Indonesian indigenous languages. Why it matters: Baldwin's contributions and leadership role at ACL highlight the growing prominence of MBZUAI and GCC-based researchers in the global NLP community.