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Reasoning with Language Model Prompting: A Survey

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arxiv 2212.09597 v8 pith:GE67QA5Z submitted 2022-12-19 cs.CL cs.AIcs.CVcs.IRcs.LG

classification cs.CLcs.AIcs.CVcs.IRcs.LG
keywords reasoningresearchlanguagemodelpromptingresourcessurveyabilities
verification ladder T0 review T1 audit T2 compute T3 formal
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Reasoning, as an essential ability for complex problem-solving, can provide back-end support for various real-world applications, such as medical diagnosis, negotiation, etc. This paper provides a comprehensive survey of cutting-edge research on reasoning with language model prompting. We introduce research works with comparisons and summaries and provide systematic resources to help beginners. We also discuss the potential reasons for emerging such reasoning abilities and highlight future research directions. Resources are available at https://github.com/zjunlp/Prompt4ReasoningPapers (updated periodically).

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 12 citations worldwide. Full citation record

  1. Reconsidering Overthinking: Penalizing Internal and External Redundancy in CoT Reasoning

    cs.AI 2025-08 conditional novelty 6.0 of 10

    A dual-penalty RL method that compresses chain-of-thought traces by separately penalizing internal semantic stagnation and external post-answer continuation reduces reasoning length by about 40% while preserving accur...

  2. E-FreeM2: Efficient Training-Free Multi-Scale and Cross-Modal News Verification via MLLMs

    cs.MM 2025-06 conditional novelty 4.0 of 10

    A training-free pipeline using image and text retrieval plus two-stage Gemini and GPT-4o mini reasoning reaches 90.0% accuracy on NewsCLIPpings out-of-context detection, but code, prompts, and error bars are missing.

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