Translate-test is a popular technique to improve the performance of multilingual language models. This approach works by translating the input into English using an external machine translation system, and running inference over the translated input. However, these improvements can be attributed to the use of a separate translation system, which is typically trained on large amounts of parallel data not seen by the language model. In this work, we introduce a new approach called self-translate, which overcomes the need of an external translation system by leveraging the few-shot translation capabilities of multilingual language models. Experiments over 5 tasks show that self-translate consistently outperforms direct inference, demonstrating that language models are unable to leverage their full multilingual potential when prompted in non-English languages. Our code is available at https://github.com/juletx/self-translate.

翻译：翻译-测试是一种提升多语言语言模型性能的常用技术。该方法通过外部机器翻译系统将输入转化为英语，并对翻译后的输入进行推理。然而，这些性能提升可能归因于独立翻译系统的使用，该系统通常基于语言模型未见过的大规模平行数据进行训练。在本研究中，我们提出了一种名为自翻译的新方法，该方法利用多语言语言模型的少样本翻译能力，无需借助外部翻译系统。在5项任务上的实验表明，自翻译始终优于直接推理，证明语言模型在非英语提示下无法充分发挥其多语言潜力。我们的代码已开源：https://github.com/juletx/self-translate。