Abstract
Bridging the significant gap between large language model's English and non-English performance presents a great challenge. While some previous studies attempt to mitigate this gap with translated training data, the recently proposed question alignment approach leverages the model's English expertise to improve multilingual performance with minimum usage of expensive, error-prone translation. In this paper, we explore how broadly this method can be applied by examining its effects in reasoning with executable code and reasoning with common sense. We also explore how to apply this approach efficiently to extremely large language models using proxy-tuning. Experiment results on multilingual reasoning benchmarks mGSM, mSVAMP and xCSQA demonstrate that the question alignment approach can be used to boost multilingual performance across diverse reasoning scenarios, model families, and sizes. For instance, when applied to the LLaMA2 models, our method brings an average accuracy improvements of 12.2% on mGSM even with the 70B model. To understand the mechanism of its success, we analyze representation space, chain-of-thought and translation data scales, which reveals how question translation training strengthens language alignment within LLMs and shapes their working patterns.
Abstract (translated)
跨越大型语言模型英语和非英语性能之间的显著差距提出了一个巨大的挑战。虽然一些以前的研究试图通过翻译训练数据来弥合这一差距，但最近提出的疑问对齐方法利用模型的英语专业知识来提高多语言性能，同时最小化使用昂贵且容易出错的翻译。在本文中，我们研究了这种方法在推理执行代码和推理与常识中的应用效果。我们还研究了如何使用代理调整来有效地应用于极其大型语言模型。在多语言推理基准测试mGSM、mSVAMP和xCSQA上进行实验结果表明，疑问对齐方法可以用于提高各种推理场景、模型家族和大小下的多语言性能。例如，当应用于LLLA2模型时，我们的方法在mGSM基准测试上平均提高了12.2%的准确性，即使只有70B模型。为了了解其成功的原因，我们分析了表示空间、推理数据规模以及翻译数据规模，揭示了疑问翻译训练如何加强LLM中的语言对齐并塑造其工作方式。
URL
https://arxiv.org/abs/2405.01345
