  We analyze the performance of large language models (LLMs) on Text Style
Transfer (TST), specifically focusing on sentiment transfer and text
detoxification across three languages: English, Hindi, and Bengali. Text Style
Transfer involves modifying the linguistic style of a text while preserving its
core content. We evaluate the capabilities of pre-trained LLMs using zero-shot
and few-shot prompting as well as parameter-efficient finetuning on publicly
available datasets. Our evaluation using automatic metrics, GPT-4 and human
evaluations reveals that while some prompted LLMs perform well in English,
their performance in on other languages (Hindi, Bengali) remains average.
However, finetuning significantly improves results compared to zero-shot and
few-shot prompting, making them comparable to previous state-of-the-art. This
underscores the necessity of dedicated datasets and specialized models for
effective TST.
