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

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arxiv 2404.15676 v3 pith:UKLD5AXN submitted 2024-04-24 cs.CL cs.AI

Yu Xia , Rui Wang , Xu Liu , Mingyan Li , Tong Yu , Xiang Chen , Julian McAuley , Shuai Li This is my paper
classification cs.CLcs.AI
keywords llmschain-of-xmethodssurveybeenchain-of-thoughttasksabilities
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 6 citations worldwide. Full citation record

  1. What makes prompts a graph: necessary and sufficient conditions for prompt graph engineering

    cs.AI 2026-07 conditional novelty 5.0 of 10

    The paper defines prompt graph engineering via four necessary and sufficient conditions (explicit structure, structure/content separation, executable semantics, first-class artifact) and an inclusion/exclusion test th...

  2. ViTCoT: Video-Text Interleaved Chain-of-Thought for Boosting Video Understanding in Large Language Models

    cs.CV 2025-07 conditional novelty 5.0 of 10

    Interleaving key video frames into step-by-step reasoning improves video question answering by 1.7 to 5.5 points over text-only chain-of-thought on a new self-built benchmark.

  3. Generating Privacy Stories From Software Documentation

    cs.SE 2025-06 conditional novelty 5.0 of 10

    LLMs can extract privacy behaviors from software documents and draft privacy stories, but the best overall F1 is 0.766, not the abstract's 0.8+.

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