Researchers at MBZUAI have introduced TiBiX, a novel approach leveraging temporal information from previous chest X-rays (CXRs) and reports for bidirectional generation of current CXRs and reports. TiBiX addresses two key challenges: generating current images from previous images and reports, and generating current reports from both previous and current images. The study also introduces a curated temporal benchmark dataset derived from the MIMIC-CXR dataset and achieves state-of-the-art results in report generation.
Researchers at MBZUAI have introduced QRAFT, an LLM-based framework designed to automate the generation of fact-checking articles. The system mimics the writing workflow of human fact-checkers, aiming to bridge the gap between automated fact-checking systems and public dissemination. While QRAFT outperforms existing text-generation methods, it still falls short of expert-written articles, highlighting areas for further research.
This research explores the use of generative AI, specifically ChatGPT, to create student assessments that align with academic accreditation standards, such as those of the National Center for Academic Accreditation in Saudi Arabia and ABET. The study introduces a method for mapping verbs used in questions to educational outcomes, enabling AI to produce and validate accreditation-compliant questions. A survey of faculty members in Saudi universities showed high acceptance rates for AI-generated exam questions and AI assistance in editing existing questions.
The paper introduces Guided Deep List, a tool for automating the generation of epidemiological line lists from open source reports. The tool uses distributed vector representations and dependency parsing to extract tabular data on disease outbreaks. It was evaluated on MERS outbreak data in Saudi Arabia, demonstrating improved accuracy over baseline methods and enabling epidemiological inferences.
This paper focuses on analyzing surveys of women entrepreneurs in the UAE using machine learning techniques. The goal is to extract relevant insights from the data to understand the current landscape and predict future trends. The study aims to support better business decisions related to women in entrepreneurship.
The paper introduces OmniGen, a unified framework for generating aligned multimodal sensor data for autonomous driving using a shared Bird's Eye View (BEV) space. It uses a novel generalizable multimodal reconstruction method (UAE) to jointly decode LiDAR and multi-view camera data through volume rendering. The framework incorporates a Diffusion Transformer (DiT) with a ControlNet branch to enable controllable multimodal sensor generation, demonstrating good performance and multimodal consistency.
The GenAI Content Detection Task 1 is a shared task on detecting machine-generated text, featuring monolingual (English) and multilingual subtasks. The task, part of the GenAI workshop at COLING 2025, attracted 36 teams for the English subtask and 26 for the multilingual one. The organizers provide a detailed overview of the data, results, system rankings, and analysis of the submitted systems.