MBZUAI Professor Chih-Jen Lin gave a keynote at the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval in Taipei. Lin's address, titled ‘On the “Rough Use” of Machine Learning Techniques’, focused on instances where machine learning techniques are employed inappropriately, using examples from graph representation learning and deep neural networks. He advocated for the development of high-quality, user-friendly software to improve the practical application of machine learning and mitigate misuse. Why it matters: Showcases MBZUAI's faculty expertise and contributions to the discussion on responsible AI research and deployment on a global stage.
Akhil Arora from EPFL presented a framework for AI-assisted knowledge navigation, focusing on understanding and enhancing human navigation on Wikipedia. The framework includes methods for modeling navigation patterns, identifying knowledge gaps, and assessing their causal impact. He also discussed applications beyond Wikipedia, such as multimodal knowledge navigation assistants and multilingual knowledge gap mitigation. Why it matters: This research has the potential to improve information systems by making online knowledge more accessible and navigable, especially for platforms like Wikipedia that serve as critical resources for global knowledge sharing.
Iryna Gurevych from TU Darmstadt presented research on using large language models for real-world fact-checking, focusing on dismantling misleading narratives from misinterpreted scientific publications and detecting misinformation via visual content. The research aims to explain why a false claim was believed, why it is false, and why the alternative is correct. Why it matters: Addressing misinformation, especially when supported by seemingly credible sources, is critical for public health, conflict resolution, and maintaining trust in institutions in the Middle East and globally.
Liangming Pan from UCSB presented research on building reliable generative AI agents by integrating symbolic representations with LLMs. The neuro-symbolic strategy combines the flexibility of language models with precise knowledge representation and verifiable reasoning. The work covers Logic-LM, ProgramFC, and learning from automated feedback, aiming to address LLM limitations in complex reasoning tasks. Why it matters: Improving the reliability of LLMs is crucial for high-stakes applications in finance, medicine, and law within the region and globally.
Iryna Gurevych from TU Darmstadt discussed challenges in using NLP for misinformation detection, highlighting the gap between current fact-checking research and real-world scenarios. Her team is working on detecting emerging misinformation topics and has constructed two corpora for fact checking using larger evidence documents. They are also collaborating with cognitive scientists to detect and respond to vaccine hesitancy using effective communication strategies. Why it matters: Addressing misinformation is crucial in the Middle East, especially regarding public health and socio-political issues, making advancements in NLP-based fact-checking highly relevant.