The recent developments in the field of natural language processing (NLP) and large language models (LLMs) have shown a significant shift towards addressing the challenges of underrepresented languages, particularly in non-English contexts. There is a growing emphasis on creating specialized datasets and benchmarks that cater to the linguistic and cultural nuances of these languages, aiming to enhance the performance and safety of AI models in diverse global settings. This trend is evident in the creation of datasets for Swahili QA, Arabic LLM safety evaluation, Arabic writing assistance, and a comprehensive Arabic multimodal benchmark. These initiatives not only aim to improve the accuracy and reliability of AI models in these languages but also to ensure ethical considerations such as data privacy, bias mitigation, and inclusivity are central to their development. The field is moving towards a more inclusive and culturally sensitive approach to AI development, with a focus on leveraging advanced technologies to support low-resource languages and regions. Notably, the creation of the Swahili QA dataset and the comprehensive Arabic multimodal benchmark stand out for their potential to significantly advance NLP research and applications in underrepresented languages.