The field of natural language processing is moving towards more inclusive and culturally diverse models, with a focus on evaluating their performance across different languages and dialects. Recent developments have highlighted the need for more robust and standardized evaluation frameworks, particularly for vision-language models. Researchers are working on creating benchmarks and toolkits that can assess the ability of models to generalize across languages and dialects, and to accurately interpret cultural elements in visual contexts. Notable papers include: JEEM, which introduces a benchmark for evaluating vision-language models in four Arabic dialects, and HRET, which presents a self-evolving evaluation toolkit for Korean large language models. KOFFVQA is also noteworthy, as it provides a free-form visual question answering benchmark in the Korean language, and LARGE introduces a tool for holistic evaluation of retrieval-augmented generation systems in the legal domain. Overall, the field is shifting towards more comprehensive and nuanced evaluations of model performance, with a focus on improving their ability to understand and generate text in a variety of languages and contexts.