This study addresses the challenge of extending Large Language Models (LLMs)
to non-English languages, specifically those using non-Latin scripts. We
propose an innovative approach that utilizes the romanized form of text as an
interface for LLMs, hypothesizing that its frequent informal use and shared
tokens with English enhance cross-lingual alignment. Focusing on Hindi, we
demonstrate through Hindi-to-English translation and sentiment analysis tasks
that romanized text not only significantly improves inference efficiency due to
its lower fertility compared to native text but also achieves competitive
performance with limited pre-training. Additionally, our novel multi-script
prompting approach, which combines romanized and native texts, shows promise in
further enhancing task performance. These findings suggest the potential of
romanization in bridging the language gap for LLM applications, with future
work aimed at expanding this approach to more languages and tasks.

本研究通过使用罗马化文本作为 LLMs 的界面来解决将大型语言模型扩展到非英语语言（尤其是使用非拉丁文字的语言）的挑战，并通过印地语到英语翻译和情感分析任务展示了罗马化文本的潜力，既提高了推理效率，又在有限的预训练情况下取得了竞争性的性能，同时将罗马化文本与原生文本结合的多脚本提示方法也显示出了提高任务性能的潜力。这些发现表明罗马化在弥合 LLM 应用中的语言障碍方面具有潜力，未来的工作将致力于将这种方法扩展到更多的语言和任务。