Multilingual NLP Models: A Review of Techniques, Tools, and Real-World Use Cases

Abstract

This survey summarized rapid developments in multilingual NLP. This growth was accelerated by increased digital communication globally. It reviewed the twenty most important works and delved into key areas such as sentiment analysis, machine translation, and hate speech detection. Most of the progress was achieved through powerful pre-trained models such as GPT-4, XLM-R, LLAMA-2, among others, and cross-lingual transfer learning. However, major challenges still remain. These are issues with low-resource languages, tokenization issues, bias within datasets, and lack of model explainability. Inconsistencies in inter-lingual evaluation also make progress harder. The paper concludes by highlighting that future efforts must focus on considerations of fairness, resource efficiency, and linguistic diversity. Realization of systems that are fair and culturally aware is critical in ensuring that such systems will be useful across the globe and meet ethical standards in NLP.