  In this study, we introduce BEnQA, a dataset comprising parallel Bengali and
English exam questions for middle and high school levels in Bangladesh. Our
dataset consists of approximately 5K questions covering several subjects in
science with different types of questions, including factual, application, and
reasoning-based questions. We benchmark several Large Language Models (LLMs)
with our parallel dataset and observe a notable performance disparity between
the models in Bengali and English. We also investigate some prompting methods,
and find that Chain-of-Thought prompting is beneficial mostly on reasoning
questions, but not so much on factual ones. We also find that appending English
translation helps to answer questions in Bengali. Our findings point to
promising future research directions for improving the performance of LLMs in
Bengali and more generally in low-resource languages.
