Chain-of-Thought (CoT) has been a widely adopted prompting method, eliciting impressive reasoning abilities of Large Language Models (LLMs). Inspired by the sequential thought structure of CoT, a number of Chain-of-X (CoX) methods have been developed to address various challenges across diverse domains and tasks involving LLMs. In this paper, we provide a comprehensive survey of Chain-of-X methods for LLMs in different contexts. Specifically, we categorize them by taxonomies of nodes, i.e., the X in CoX, and application tasks. We also discuss the findings and implications of existing CoX methods, as well as potential future directions. Our survey aims to serve as a detailed and up-to-date resource for researchers seeking to apply the idea of CoT to broader scenarios.

中文翻译：

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超越思想链：法学硕士 Chain-of-X 范式调查

思想链（CoT）是一种广泛采用的提示方法，引发了大型语言模型（LLM）令人印象深刻的推理能力。受 CoT 顺序思维结构的启发，开发了许多 Chain-of-X (CoX) 方法来解决涉及法学硕士的不同领域和任务的各种挑战。在本文中，我们对不同背景下法学硕士的 Chain-of-X 方法进行了全面的调查。具体来说，我们通过节点分类法（即 CoX 中的 X）和应用程序任务对它们进行分类。我们还讨论了现有 CoX 方法的发现和影响，以及未来潜在的方向。我们的调查旨在为寻求将 CoT 理念应用到更广泛场景的研究人员提供详细且最新的资源。