  This study addresses the challenge of extending Large Language Models (LLMs)
to non-English languages that use non-Roman scripts. We propose an 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. Our approach involves the continual
pretraining of an English LLM like Llama 2 on romanized text of non-English,
non-Roman script languages, followed by instruction tuning on romanized data.
The results indicate that romanized text not only reduces token fertility by
2x-4x but also matches or outperforms native script representation across
various NLU, NLG, and MT tasks. Moreover, the embeddings computed on romanized
text exhibit closer alignment with their English translations than those from
the native script. Our approach presents a promising direction for leveraging
the power of English LLMs in languages traditionally underrepresented in NLP.
Our code is available on https://github.com/AI4Bharat/romansetu.
