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Over the last few years, large language models (LLMs) have emerged as themost important breakthroughs in natural language processing (NLP) thatfundamentally transform research and developments in the field. ChatGPTrepresents one of the most exciting LLM systems developed recently to showcaseimpressive skills for language generation and highly attract public attention.Among various exciting applications discovered for ChatGPT in English, themodel can process and generate texts for multiple languages due to itsmultilingual training data. Given the broad adoption of ChatGPT for English indifferent problems and areas, a natural question is whether ChatGPT can also beapplied effectively for other languages or it is necessary to develop morelanguage-specific technologies. The answer to this question requires a thoroughevaluation of ChatGPT over multiple tasks with diverse languages and largedatasets (i.e., beyond reported anecdotes), which is still missing or limitedin current research. Our work aims to fill this gap for the evaluation ofChatGPT and similar LLMs to provide more comprehensive information formultilingual NLP applications. While this work will be an ongoing effort toinclude additional experiments in the future, our current paper evaluatesChatGPT on 7 different tasks, covering 37 diverse languages with high, medium,low, and extremely low resources. We also focus on the zero-shot learningsetting for ChatGPT to improve reproducibility and better simulate theinteractions of general users. Compared to the performance of previous models,our extensive experimental results demonstrate a worse performance of ChatGPTfor different NLP tasks and languages, calling for further research to developbetter models and understanding for multilingual learning.

ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning