@inproceedings{xia-etal-2025-beyond,
title = "Beyond Chain-of-Thought: A Survey of Chain-of-{X} Paradigms for {LLM}s",
author = "Xia, Yu and
Wang, Rui and
Liu, Xu and
Li, Mingyan and
Yu, Tong and
Chen, Xiang and
McAuley, Julian and
Li, Shuai",
editor = "Rambow, Owen and
Wanner, Leo and
Apidianaki, Marianna and
Al-Khalifa, Hend and
Eugenio, Barbara Di and
Schockaert, Steven",
booktitle = "Proceedings of the 31st International Conference on Computational Linguistics",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.coling-main.719/",
pages = "10795--10809",
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 challenges across diverse domains and tasks. 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."
}
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<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 challenges across diverse domains and tasks. 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>
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%0 Conference Proceedings
%T Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs
%A Xia, Yu
%A Wang, Rui
%A Liu, Xu
%A Li, Mingyan
%A Yu, Tong
%A Chen, Xiang
%A McAuley, Julian
%A Li, Shuai
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F xia-etal-2025-beyond
%X 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 challenges across diverse domains and tasks. 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.
%U https://aclanthology.org/2025.coling-main.719/
%P 10795-10809
Markdown (Informal)
[Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs](https://aclanthology.org/2025.coling-main.719/) (Xia et al., COLING 2025)
ACL
- Yu Xia, Rui Wang, Xu Liu, Mingyan Li, Tong Yu, Xiang Chen, Julian McAuley, and Shuai Li. 2025. Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs. In Proceedings of the 31st International Conference on Computational Linguistics, pages 10795–10809, Abu Dhabi, UAE. Association for Computational Linguistics.