The field of natural language processing (NLP) is moving towards improved handling of low-resource languages, with a focus on developing more effective models and techniques for languages with limited computational resources. Recent research has highlighted the importance of tailored tokenization methods, morphological analysis, and language-specific modeling approaches for these languages. The development of new benchmarks and evaluation frameworks, such as TR-MMLU and GRILE, has also enabled more accurate assessment of model performance in low-resource languages. Notable papers in this area include: Overcoming Low-Resource Barriers in Tulu, which presents a benchmark dataset for Offensive Language Identification in code-mixed Tulu social media content and evaluates the performance of various deep learning models. UNVEILING: What Makes Linguistics Olympiad Puzzles Tricky for LLMs, which analyzes the performance of large language models on linguistics puzzles and identifies areas for improvement in linguistic reasoning and modeling of low-resource languages. DocHPLT: A Massively Multilingual Document-Level Translation Dataset, which introduces a large-scale document-level translation dataset and demonstrates its effectiveness in improving the performance of large language models on document-level translation tasks. Tokens with Meaning: A Hybrid Tokenization Approach for NLP, which proposes a novel hybrid tokenization framework that combines rule-based morphological analysis with statistical subword segmentation and achieves state-of-the-art results on the TR-MMLU benchmark.