超越思维链：面向大语言模型的 Chain-of-X 范式综述
Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs
AI总结：
本文系统综述了受思维链启发的 Chain-of-X 方法，按节点类型和应用任务分类，总结现有发现并展望未来方向，为将 CoT 推广到更广泛场景提供参考。
摘要：
思维链（CoT）已成为一种被广泛采用的提示方法，激发了大语言模型（LLMs）令人瞩目的推理能力。受 CoT 顺序化思维结构的启发，大量 Chain-of-X（CoX）方法被提出，用于应对涉及 LLMs 的不同领域和任务中的各种挑战。本文对不同语境下用于 LLMs 的 Chain-of-X 方法进行了全面综述。具体而言，我们依据节点分类法（即 CoX 中的 X）和应用任务对其进行分类。我们还讨论了现有 CoX 方法的发现与启示，以及未来可能的研究方向。本综述旨在为希望将 CoT 思想应用于更广泛场景的研究者提供一份详尽且及时的资源。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
