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.
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.
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.
An MBZUAI team won the best paper award at the inaugural Arabic Natural Language Processing Conference for their work on processing Arabic speech. Their study establishes a new approach to tackle the complexities of spoken Arabic, which differs significantly from text-based language models. The team's approach aims to advance new tools for Arabic speakers by addressing challenges like intonation and the continuous nature of speech. Why it matters: This award highlights the importance of specialized research in Arabic NLP, as mainstream LLMs often face limitations in accurately processing the nuances of Arabic speech.
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.