Is Translation All You Need? A Study on Solving Multilingual Tasks with
  Large Language Models
Chaoqun Liu Wenxuan Zhang Yiran Zhao Anh Tuan Luu Lidong Bing
Large language models (LLMs) have demonstrated strong multilingual
capabilities; yet, they are mostly English-centric due to the imbalanced
training corpora. Existing works leverage this phenomenon to improve their
multilingual performances on NLP tasks. In this work, we extend the evaluation
from NLP tasks to real user queries. We find that even though translation into
English can help improve the performance of multilingual NLP tasks for
English-centric LLMs, it may not be optimal for all scenarios. For
culture-related tasks that need deep language understanding, prompting in the
native language proves to be more promising since it can capture the nuances
related to culture and language. Therefore, we advocate for more efforts
towards the development of strong multilingual LLMs instead of just
English-centric LLMs.