Could Thinking Multilingually Empower LLM Reasoning?
Authors: name: Changjiang Gao, name: Xu Huang, name: Wenhao Zhu, name: Shujian Huang, name: Lei Li, name: Fei Yuan

Previous work indicates that large language models exhibit a significant
"English bias", i.e. they often perform better when tasks are presented in
English. Interestingly, we have observed that using certain other languages in
reasoning tasks can yield better performance than English. However, this
phenomenon remains under-explored. In this paper, we explore the upper bound of
harnessing multilingualism in reasoning tasks, suggesting that multilingual
reasoning promises significantly (by nearly 10 Acc@k points) and robustly
(tolerance for variations in translation quality and language choice) higher
upper bounds than English-only reasoning. Besides analyzing the reason behind
the upper bound and challenges in reaching it, we also find that common answer
selection methods cannot achieve this upper bound, due to their limitations and
biases. These insights could pave the way for future research aimed at fully
harnessing the potential of multilingual reasoning in LLMs.