Large language models (LLMs) with Chain-of-thought (CoT) have recently
emerged as a powerful technique for eliciting reasoning to improve various
downstream tasks. As most research mainly focuses on English, with few
explorations in a multilingual context, the question of how reliable this
reasoning capability is in different languages is still open. To address it
directly, we study multilingual reasoning consistency across multiple
languages, using popular open-source LLMs. First, we compile the first
large-scale multilingual math reasoning dataset, mCoT-MATH, covering eleven
diverse languages. Then, we introduce multilingual CoT instruction tuning to
boost reasoning capability across languages, thereby improving model
consistency. While existing LLMs show substantial variation across the
languages we consider, and especially low performance for lesser resourced
languages, our 7B parameter model mCoT achieves impressive consistency across
languages, and superior or comparable performance to close- and open-source
models even of much larger sizes.

通过多语言推理一致性研究，我们构建了第一个大规模的多语言数学推理数据集 mCoT-MATH，然后引入多语言 CoT 指令调节以提高模型的推理能力和一致性。我们的 7B 参数模型 mCoT 在各种语言上展现出惊人的一致性，并且在性能上优于或与更大规模的闭源和开源模型相媲美。

mCoT: 多语言指导调整以提升语言模型的推理一致性

mCoT: Multilingual Instruction Tuning for Reasoning Consistency in  Language Models

Large language models show compelling performance on reasoning tasks but they
tend to perform much worse in languages other than English. This is
unsurprising given that their training data largely consists of English text
and instructions. A typical solution is to translate instruction data into all
languages of interest, and then train on the resulting multilingual data, which
is called translate-training. This approach not only incurs high cost, but also
results in poorly translated data due to the non-standard formatting of
chain-of-thought and mathematical reasoning instructions. In this paper, we
explore the benefits of question alignment, where we train the model to
translate reasoning questions into English by finetuning on X-English question
data. In this way we perform targetted, in-domain language alignment which
makes best use of English instruction data to unlock the LLMs' multilingual
reasoning abilities. Experimental results on LLaMA2-13B show that question
alignment leads to consistent improvements over the translate-training
approach: an average improvement of 11.3\% and 16.1\% accuracy across ten
languages on the MGSM and MSVAMP maths reasoning benchmarks (The project will
be available at: this https URL).

通过问题对齐，我们训练模型将推理问题翻译成英文，以利用英文指导数据，提高大型语言模型在多语言推理任务上的性能。

更好的多语言推理的问题翻译训练

Question Translation Training for Better Multilingual Reasoning

Though reasoning abilities are considered language-agnostic, existing LLMs
exhibit inconsistent reasoning abilities across different languages, e.g.,
reasoning in a pivot language is superior to other languages due to the
imbalance of multilingual training data.To enhance reasoning abilities in
non-pivot languages, we propose an alignment-as-preference optimization
framework. Specifically, we adopt an open-source translation model to estimate
the consistency between answers in non-pivot and pivot languages. We further
adopt the answer consistency as the preference for DPO or PPO thus optimizing
the lesser reasoning. Experiments show that our method significantly improves
the model's multilingual reasoning, with better reasoning consistency across
languages. Our framework achieved a 13.7% accuracy improvement on out-of-domain
datasets MSVAMP while preserving the competitive performance on MGSM. Moreover,
we find that iterative DPO is helpful for further alignment and improvement of
the model's multilingual mathematical reasoning ability, further pushing the
improvement to 16.7%

通过采用一种对齐作为优选优化框架，我们在非中心语言中提高了推理能力，推理一致性得到了改善，并通过迭代 DPO 进一步优化了模型的多语言数学推理能力。

MAPO：通过多语言对齐优化推进多语言推理

MAPO: Advancing Multilingual Reasoning through Multilingual  Alignment-as-Preference Optimization

We evaluate the reasoning abilities of large language models in multilingual
settings. We introduce the Multilingual Grade School Math (MGSM) benchmark, by
manually translating 250 grade-school math problems from the GSM8K dataset
(Cobbe et al., 2021) into ten typologically diverse languages. We find that the
ability to solve MGSM problems via chain-of-thought prompting emerges with
increasing model scale, and that models have strikingly strong multilingual
reasoning abilities, even in underrepresented languages such as Bengali and
Swahili. Finally, we show that the multilingual reasoning abilities of language
models extend to other tasks such as commonsense reasoning and word-in-context
semantic judgment. The MGSM benchmark is publicly available at
this https URL.

通过使用多种类型不同的语言，我们通过手动将 GSM8K 数据集中的 250 个小学数学问题翻译成十种不同的语言，评估了大型语言模型在多语种环境下的推理能力，并提出了 MGSM 基准。我们发现，随着模型规模的增加，使用思维链提示解决 MGSM 问题的能力越来越强，即使在孟加拉语和斯瓦希里语等少数语言中，这些模型也具有非常强的多语种推理能力。最后，我们展示了语言模型的多语种推理能力扩展到其他任务，例如常识推理和上下文语义判断。