Could Thinking Multilingually Empower LLM Reasoning?
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.
6805bb01747a412bca737b53
Changjiang Gao
6805bb01747a412bca737b54
Xu Huang
6805bb01747a412bca737b55
Wenhao Zhu
6805bb01747a412bca737b56
Shujian Huang
6805bb01747a412bca737b57
Lei Li
6805bb01747a412bca737b58
Fei Yuan
Could Thinking Multilingually Empower LLM Reasoning?
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.
Multilingual reasoning in large language models shows significant potential for improved performance in reasoning tasks compared to English-only models.
large language models English bias multilingual reasoning translation quality answer selection methods