Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs
<|reference_start|>Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs: 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.<|reference_end|>
@article{xia2024beyond,
    title={Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs},
    author={Yu Xia, Rui Wang, Xu Liu, Mingyan Li, Tong Yu, Xiang Chen, Julian
  McAuley, Shuai Li},
    journal={arXiv preprint arXiv:2404.15676},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15676},
    primaryClass={cs.CL cs.AI}
}