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