Abstract
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.
Abstract (translated)
链式思考（CoT）是一种广泛采用的提示方法，激发了大型语言模型（LLMs）惊人的推理能力。受到CoT序列思维结构的启发，许多链式思考（CoX）方法为处理各种涉及LLM的多样领域和任务的挑战提供了方法。在本文中，我们对不同上下文中的链式思考（CoX）方法进行了全面的调查。具体来说，我们将它们按照节点分类法进行分类，即CoX中的X，以及应用任务。我们还讨论了现有CoX方法的发现和影响，以及潜在的未来方向。我们的调查旨在为研究人员提供详细的、最新的资源，以便他们把链式思考（CoT）应用到更广泛的场景中。
URL
https://arxiv.org/abs/2404.15676
