While reasoning and multilingual capabilities in language models (LMs) have achieved remarkable progress in recent years, their integration into a unified paradigm - multilingual reasoning - is at a nascent stage. Multilingual reasoning requires language models to handle logical reasoning across languages while addressing misalignment, biases, and challenges in low-resource settings. This survey provides the first in-depth review of multilingual reasoning in LMs. In this survey, we provide a systematic overview of existing methods that leverage LMs for multilingual reasoning, specifically outlining the challenges, motivations, and foundational aspects of applying language models to reason across diverse languages. We provide an overview of the standard data resources used for training multilingual reasoning in LMs and the evaluation benchmarks employed to assess their multilingual capabilities. Next, we analyze various state-of-the-art methods and their performance on these benchmarks. Finally, we explore future research opportunities to improve multilingual reasoning in LMs, focusing on enhancing their ability to handle diverse languages and complex reasoning tasks. Rapid growth of evolving developments in this field can be actively tracked on our project page: [https://github.com/AkashGhosh/Survey-of-Multilingual-Reasoning-in-Language-Models](https://github.com/AkashGhosh/Survey-of-Multilingual-Reasoning-in-Language-Models)

翻译：尽管语言模型（LMs）的推理能力和多语言能力近年来取得了显著进展，但将二者整合为统一范式——多语言推理——仍处于起步阶段。多语言推理要求语言模型能够跨语言处理逻辑推理，同时应对低资源环境下的错位、偏见及各类挑战。本文首次对语言模型中的多语言推理进行了深入综述。我们系统性地梳理了现有利用语言模型进行多语言推理的方法，重点阐述了跨语言推理面临的挑战、研究动机及基础性问题。本文概述了用于训练语言模型多语言推理能力的标准数据资源，以及评估其多语言能力的基准测试。随后，我们分析了各类前沿方法及其在这些基准测试上的表现。最后，我们探讨了未来提升语言模型多语言推理能力的研究方向，重点关注增强模型处理多样语言和复杂推理任务的能力。本领域的快速发展动态可通过我们的项目页面持续追踪：[https://github.com/AkashGhosh/Survey-of-Multilingual-Reasoning-in-Language-Models](https://github.com/AkashGhosh/Survey-of-Multilingual-Reasoning-in-Language-Models)