LANGUAGE MODELS ARE MULTILINGUAL CHAIN-OF-THOUGHT REASONERS

Abstract

We evaluate the reasoning abilities of large language models in multilingual settings. We introduce the Multilingual Grade School Math (MGSM) benchmark, by manually translating 250 grade-school math problems from the GSM8K dataset (Cobbe et al., 2021) into ten typologically diverse languages. We find that the ability to solve MGSM problems via chain-of-thought prompting emerges with increasing model scale, and that models have strikingly strong multilingual reasoning abilities, even in underrepresented languages such as Bengali and Swahili. Finally, we show that the multilingual reasoning abilities of language models extend to other tasks such as commonsense reasoning and wordin-context semantic judgment.



Figure1: Correlation between language frequency and MGSM accuracy for PaLM-540B. The accuracy is surprisingly high, even for underrepresented languages like Swahili (SW) and Bengali (BN), which account for less than 0.01% of the pre-training dataset.

