Abstract

Exploring the efficacy of multilingual prompt engineering for sentiment analysis reveals a promising avenue for extending the adaptability of large language models (LLMs) beyond the confines of the primary predominant English. The core ambition revolves around devising strategies for transferring adept English instructions into the target language. These strategies exploit the remarkable capability of large language models to extract information and learn new task by the context of a few demonstrations – known as in-context learning. In this research, the strategies leverage both monolingual and cross-lingual prompt templates, augmented with demonstrations. Furthermore, the process of instruction generation is supported by an iterative rephrasing approach that refines instructions into their optimal counterparts.

The investigation unfolds through a careful analysis of how multilingual instruction generation benefits from incorporating demonstrations, either in English or the target language, within the prompt template. Results substantiate that iteratively rephrasing instructions further improves the effectiveness of the instruction generation process, underscoring the proficiency of large language models to follow the request.

Through this exploration, it emerges that the automatic prompt engineering methods exhibit potential in multilingual contexts. The findings advocate for a broader utilization of demonstration learning and iterative refinement techniques in multilingual prompt engineering, aiming to universalize the application of large language model across diverse communities and languages. This study not only fills the gap identified in previous research regarding the effectiveness of automatic prompt engineering methods for non-English languages but also facilitates broader access for linguistic communities to generative AI.