The field of large language models (LLMs) is rapidly evolving, with a growing focus on adaptability and efficiency. Recent developments have highlighted the importance of enabling LLMs to learn from limited annotations and adapt to new languages and tasks with minimal resources. This trend is driven by the need to improve the performance of LLMs in low-resource language scenarios and to enhance their transferability across different languages and tasks. Notably, innovative approaches such as knowledge transfer modules, parameter-efficient fine-tuning strategies, and adapter-based transfer methods have shown promising results. Furthermore, the development of comprehensive evaluation frameworks and polyglot language learning systems has the potential to significantly advance the field. Noteworthy papers include: GraphLAMA, which proposes a method for efficient adaptation of graph language models with limited annotations, achieving state-of-the-art performance with improved inference speed. Thunder-LLM, which presents a cost-efficient approach to adapting LLMs to new languages, achieving superior performance in Korean with minimal data and computational resources.