Large language model based machine translation for universal multilingual understanding and translation quality enhancement

Large language models based machine translation has significantly improved the fluency, adequacy, and context awareness of translations across various languages and domains. This enhancement has been achieved through comprehensive research efforts. The primary objective of this paper is to present a detailed analysis of large language model-based machine translation. We also accomplished the comprehensive compilation of different large language model-based machine translation approaches, datasets, and assessment criteria. Along with the comparative analysis with the contextual behaviors, we also identified different research gaps that may be useful for the future research of the natural language processing research community. The primary objective of this study is to determine suitable methods for enhancing translation adequacy and fluency based on the situations. In this context, three research questions are raised in the study with three objectives. One issue is whether the use of large language models (LLM) in machine translation (MT) can improve the adequacy, fluency, and ambiguity resolution. We also analysed different multimodal machine translation approaches with large language models.