## 6 Utility of Work  
The purpose of this research is to enhance natural language processing's (NLP) effectiveness and efficiency in multilingual contexts. Transfer learning and cross-lingual contextualized embeddings can be used to lessen the requirement for expensive and time-consuming acquisition of vast volumes of labeled data in each language. We can improve performance in a variety of activities, including machine translation, sentiment analysis, text categorization, and information retrieval, by utilizing knowledge from one language to another.  
Expanding on the significance of our research involving mBART and XLM-Roberta, it is imperative to acknowledge their broader implications beyond aiding less widely spoken languages. Both mBART and XLM-Roberta share foundational attributes with prominent language models (LLMs), rendering our findings pertinent to the broader landscape of language understanding.  
For instance, both mBART and XLM-Roberta leverage multi-headed attention mechanisms, a fundamental component prevalent in several prominent LLMs, such as BERT and GPT. Consequently, insights garnered from our study on mBART and XLM-Roberta are inherently transferrable to other LLMs employing analogous attention mechanisms. This underscores the potential for our research to contribute meaningfully to the collective understanding of language modeling techniques.  
Moreover, these models employ word embeddings to encapsulate semantic and contextual information, enhancing their ability to comprehend textual data. While the specifics of these embeddings may vary between models, the underlying principles remain consistent across diverse LLMs. Consequently, our investigation into the embeddings utilized by mBART and XLM-Roberta provides valuable insights applicable to enhancing the performance of other LLMs, fostering advancements in language understanding capabilities. Our research highlights the potential for these embeddings to inherit biases or limitations introduced through translation, impacting the generalizability of LLMs. This knowledge can inform the future development of LLMs, leading to more robust and nuanced models.  
Notably, despite the emergence of newer models, mBART and XLM-Roberta remain extensively used in industry applications such as summarization and next-word prediction tasks. This continued utilization stems from the high computational costs associated with employing LLMs, making mBART and XLM-Roberta attractive choices due to their balance between efficiency and performance. By acknowledging the practical significance of these models in industry settings, our research underscores their enduring relevance and applicability in addressing real-world language processing challenges.  
The value of this research lies in its potential to enhance cross-language communication and understanding of large language models. This is crucial as large language models (LLMs) are increasingly shaping how people access information. Recent work by [PERSON] et al. [18] has shown significant disparities in the accuracy, consistency, and reliability of LLM responses across languages like English, Spanish, Chinese, and Hindi, which proves that there is a need for further research in this field.