The field of natural language processing is moving towards improving the interpretability and multilingual capabilities of large language models. Recent research has focused on understanding how training samples influence model decisions and auditing large-scale datasets. Additionally, there is a growing interest in developing language-aware tokenization methods for morphologically rich scripts and exploring the role of multi-head self-attention in supporting multilingual processing. The use of positional encodings, morphological complexity, and word order flexibility is also being investigated to better understand their impact on language modeling. Noteworthy papers include: Evaluating Subword Tokenization Techniques for Bengali, which presents a new Byte Pair Encoding tokenizer specifically developed for the Bengali script, and Focusing on Language, which proposes a method to identify attention head importance for multilingual capabilities in large language models. Furthermore, research on low-resource languages such as Bangla is gaining attention, with the introduction of new datasets and models for tasks like authorship attribution and sign language translation. Overall, the field is advancing towards more transparent, efficient, and inclusive language models.