Navigating Multilingual Communication: Comparative Analysis of Translating Prompts and Responses Versus Using Language-Specific LLMs

This research paper explores the efficiency of translating prompts and responses versus using language-specific Large Language Models (LLMs). The study compares training time. It aims to identify optimal methods for multilingual communication in diverse contexts, offering insights into practical implications. In this study, the research methodology involves translating a prompt from a designated language to English, generating a response from Large Language Models (LLMs), and subsequently translating the response back to Original Language to measure time efficiency. Additionally, the study includes timing the LLM response directly to compare efficiency. Cost analysis of training a language-specific LLM vs a Multilingual LLM in terms of time will contribute to a comprehensive evaluation of efficiency. The combined results will be systematically reviewed to determine the most efficient approach. Multilingual LLMs took 44.87% less training time than sum of Individual training duration. Translation approach took 54.61% less time in responding than Multilingual LLMs. Given that Multilingual Large Language Models (LLMs) require approximately half the time for training but take more time to respond compared to the Translated approach, it appears that the Translation approach is more efficient. Therefore, it is advisable to use the Translation approach more frequently due to its efficiency.