  This paper explores the potential of leveraging Large Language Models (LLMs)
for data augmentation in multilingual commonsense reasoning datasets where the
available training data is extremely limited. To achieve this, we utilise
several LLMs, namely Dolly-v2, StableVicuna, ChatGPT, and GPT-4, to augment
three datasets: XCOPA, XWinograd, and XStoryCloze. Subsequently, we evaluate
the effectiveness of fine-tuning smaller multilingual models, mBERT and XLMR,
using the synthesised data. We compare the performance of training with data
generated in English and target languages, as well as translated
English-generated data, revealing the overall advantages of incorporating data
generated by LLMs, e.g. a notable 13.4 accuracy score improvement for the best
case. Furthermore, we conduct a human evaluation by asking native speakers to
assess the naturalness and logical coherence of the generated examples across
different languages. The results of the evaluation indicate that LLMs such as
ChatGPT and GPT-4 excel at producing natural and coherent text in most
languages, however, they struggle to generate meaningful text in certain
languages like Tamil. We also observe that ChatGPT falls short in generating
plausible alternatives compared to the original dataset, whereas examples from
GPT-4 exhibit competitive logical consistency.
