Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs
This is an incremental survey that organizes existing methods to help researchers apply CoT ideas more broadly.
The paper surveys Chain-of-X (CoX) methods, which extend Chain-of-Thought prompting to address various challenges in LLMs across domains, categorizing them by taxonomies and tasks to provide a comprehensive resource for researchers.
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.