Beyond Chain-of-Thought: A Survey of Chain-of-X Paradigms for LLMs

TL;DR

This survey reviews Chain-of-X methods for Large Language Models, expanding on Chain-of-Thought prompting by categorizing various approaches and discussing their applications, findings, and future directions.

Contribution

It provides a comprehensive taxonomy and analysis of Chain-of-X methods, broadening the understanding of reasoning techniques in LLMs beyond traditional Chain-of-Thought.

Findings

CoX methods enhance reasoning across diverse tasks

Taxonomy helps categorize and compare CoX approaches

Future research directions identified for CoX methods

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.