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.
View on arXiv@article{xia2025_2404.15676,
title={ Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs },
author={ Yu Xia and Rui Wang and Xu Liu and Mingyan Li and Tong Yu and Xiang Chen and Julian McAuley and Shuai Li },
journal={arXiv preprint arXiv:2404.15676},
year={ 2025 }
}