## 2 Related Work
A notable example of this is in the multilingual epidemiological text classification field, which has been explored using a variety of machine learning approaches, including advanced deep learning techniques [15]. This research spanned six languages and demonstrated the superior performance of BERT models, which were fine-tuned for the specific requirements of epidemiological data analysis. These models significantly outperformed other techniques, showcasing their robust adaptability across diverse linguistic datasets. Further evidence of the effectiveness of multilingual models can be seen in research addressing sentiment classification and hate speech detection [16]. This study explored the application of these models across four languages for sentiment classification and two for hate speech detection, finding that multilingual models, especially those based on the XLM-R architecture, consistently outperformed their monolingual counterparts. Such findings underscore the potential of multilingual models to provide more uniform and effective solutions across different languages and content types. Additional research with the Sinhala language has further reinforced the advantages of multilingual models in handling a variety of linguistic tasks such as sentiment analysis, news category classification, writing style analysis, and news source identification [17]. In these cases, models like XLM-R, LaBSE, and LASER were compared against monolingual models specifically developed for the Sinhala language, such as SinBERTo and SinhalaBERTo. The multilingual models, particularly XLM-R, consistently delivered superior performance across all tasks, highlighting their effectiveness in leveraging crosslingual knowledge. The provers of XLM-R was further demonstrated in a study involving product categorization and sentiment analysis across multiple datasets covering three and six languages, respectively [18]. In these experiments, XLM-R outperformed other models, including mBERT and DistilmBERT, as well as various zero-shot models. Its capabilities were even further enhanced when fine-tuned on a diverse set of multilingual Twitter data that included over 60 languages [19]. The resultant model, named XLM-T, was applied to sentiment classification tasks across eight languages and proved even more effective than the standard XLM-R, offering improved performance and demonstrating the model's ability to adapt to an even broader array of linguistic contexts. These studies collectively illustrate the critical role of multilingual models in modern text classification, particularly their capacity to transcend language barriers and provide scalable, effective solutions for a wide range of text-based applications. The ongoing advancements in model training and fine-tuning are likely to enhance their applicability further, making them even more central to the future of multilingual text processing. After delving into the capabilities and challenges of multilingual models, our research turns its attention to crosslingual models. These models are distinctive because they are generally trained in a dominant language, often English, and are then applied to different languages without retraining. Such an approach allows for leveraging high-quality, abundant resources available in English to benefit less-resourced languages. However, it is important to acknowledge that crosslingual models often experience a drop in accuracy compared to their monolingual counterparts, a phenomenon well documented in the literature [20]. This accuracy dip is partly due to the intrinsic variability and complexity of language translation and contextual usage across languages. Some crosslingual strategies rely heavily on translated knowledge to bridge language gaps.