The field of natural language processing (NLP) has grown rapidly in recent years, creating a pressing need for better datasets to train large language models (LLMs). Multilingual models, in particular, require datasets that are not only large but also diverse and carefully curated to capture the nuances of many different languages. Existing resources like CC-100,… →
Web-crawled image-text datasets are critical for training vision-language models, enabling advancements in tasks such as image captioning and visual question answering. However, these datasets often suffer from noise and low quality, with inconsistent associations between images and text that limit the capabilities of the models. This limitation prevents achieving strong and accurate results, particularly in… →
Code intelligence has grown rapidly, driven by advancements in large language models (LLMs). These models are increasingly utilized for automated programming tasks such as code generation, debugging, and testing. With capabilities spanning multiple languages and domains, LLMs have become crucial tools in advancing software development, data science, and computational problem-solving. The evolution of LLMs is… →
Large language models (LLMs) have profoundly influenced natural language processing (NLP), excelling in tasks like text generation and language understanding. However, the Arabic language—with its intricate morphology, varied dialects, and cultural richness—remains underrepresented. Many advanced LLMs are designed with English as their primary focus, leaving Arabic-centric models either overly large and computationally demanding or inadequate… →
Board games have long been pivotal in shaping AI, serving as structured environments for testing decision-making and strategy. Games like chess and Connect Four, with their distinct rules and varying levels of complexity, have enabled AI systems to learn dynamic problem-solving. The structured nature of these games challenges AI to anticipate moves, consider opponents’ strategies,… →
Reward modeling is critical in aligning LLMs with human preferences, particularly within the reinforcement learning from human feedback (RLHF) framework. Traditional reward models (RMs) assign scalar scores to evaluate how well LLM outputs align with human judgments, guiding optimization during training to improve response quality. However, these models often need more interpretability, are prone to… →
CONCLUSION AND DISCUSSION: Overall, our findings suggest that e-TRE is more effective than m-TRE for losing weight and reducing insulin resistance in patients with polycystic ovary syndrome. However, results on lipid profile are conflicting, and further randomized control trials are needed. →
INTRODUCTION: Tuberculosis (TB) continues to be one of the deadliest infectious diseases over the centuries, killing more people worldwide than any other single infectious disease. There is an urgent need for additional strategies which can expedite efforts to combat TB including a preventive vaccine. In this endeavour, we have developed a protocol for a multisite,… →
BACKGROUND: The rise in the number of children diagnosed with attention-deficit/hyperactivity disorder (ADHD) highlights the need for effective interventions targeting attentional control. Although recent research has demonstrated the potential of neurofeedback training (NFT) for children with ADHD, most studies have been conducted in laboratory settings, raising questions about their real-world applicability. To address this issue,… →
Business intelligence (BI) faces significant challenges in efficiently transforming large data volumes into actionable insights. Current workflows involve multiple complex stages, including data preparation, analysis, and visualization, which require extensive collaboration among data engineers, scientists, and analysts using diverse specialized tools. These processes are time-consuming and tedious, demanding significant manual intervention and coordination. The intricate… →