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Towards Better Chain-of-Thought Prompting Strategies: A Survey
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Chain-of-Thought (CoT), a step-wise and coherent reasoning chain, shows its impressive strength when used as a prompting strategy for large language models (LLM). Recent years, the prominent effect of CoT prompting has attracted emerging research. However, there still lacks of a systematic summary about key factors of CoT prompting and comprehensive guide for prompts utilizing. For a deeper understanding about CoT prompting, we survey on a wide range of current research, presenting a systematic and comprehensive analysis on several factors that may influence the effect of CoT prompting, and introduce how to better apply it in different applications under these discussions. We further analyze the challenges and propose some future directions about CoT prompting. This survey could provide an overall reference on related research.
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Cited by 9 Pith papers
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What Does Chain-of-Thought Contribute at Probe Time? Evidence for Local Co-Occurrence Activation
CoT probe-time gains arise primarily from lexical activation and short-range token co-occurrence rather than sentence-level logical derivation.
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Benchmarking LLM-Assisted Blue Teaming via Standardized Threat Hunting
Standardized modular threat-hunting workflows (CyberTeam) improve LLM performance on blue team tasks compared to open-ended ICL, CoT, and ToT prompting across 30 tasks and 452k samples.
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VaccineRAG: Boosting Multimodal Large Language Models' Immunity to Harmful RAG Samples
A CoT-annotated dataset (VaccineRAG) plus segment-level GRPO (Partial-GRPO) improves multimodal large language models' ability to ignore harmful retrieved samples in retrieval-augmented generation tasks.
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Synthetic Heuristic Evaluation: A Comparison between AI- and Human-Powered Usability Evaluation
An LLM prompted to conduct heuristic evaluation reported more usability issues on two apps than five human experts, but the ground truth included the LLM's own findings.
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PixelThink: Towards Efficient Chain-of-Pixel Reasoning
A soft token-budget penalty driven by task difficulty and model confidence roughly halves the reasoning tokens used by segmentation MLLMs while slightly improving mask accuracy on ReasonSeg-derived benchmarks.
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Select, Read, and Write: A Multi-Agent Framework of Full-Text-based Related Work Generation
A multi-agent reader-selector-writer framework improves generated related-work sections by reading full texts in a graph-guided order and compressing key information into shared memory.
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Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial
A survey and tutorial that organizes LLM-enabled wireless network optimization into formulation, solution, and verification stages, with case studies drawn from the authors' own prior papers.
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Graph-of-Causal Evolution: Challenging Chain-of-Model for Reasoning
GoCE swaps CoM's chain structure for a differentiable causal graph and reports accuracy gains on CLUTRR, CLadder, EX-FEVER, and CausalQA, but the evidence is sandbox-generated and unauditable.
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Chain-of-Thought for Autonomous Driving: A Comprehensive Survey and Future Prospects
A survey that classifies chain-of-thought methods for autonomous driving into modular, logical, and reflective pipelines, and proposes three evolutionary stages from direct prompting to reinforcement learning.
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