Pith. sign in

REVIEW 6 cited by

Reasoning with Language Model Prompting: A Survey

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

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
0 comments
read the original abstract

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).

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 6 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. Which Data Attributes Stimulate Math and Code Reasoning? An Investigation via Influence Functions

    cs.LG 2025-05 conditional novelty 6.0 of 10

    Influence-function attribution shows high-difficulty math and low-difficulty code training data best improve math and code reasoning, and difficulty-based reweighting improves benchmark performance.

  3. CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction

    cs.CL 2025-02 conditional novelty 6.0 of 10

    Training on 3.5M code input-output prediction tasks with natural-language chain-of-thought improves LLM performance on math, logic, symbolic, scientific, and commonsense reasoning benchmarks.

  4. Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers

    cs.CL 2025-05 conditional novelty 5.0 of 10

    EXSEARCH trains LLMs for agentic search by treating search trajectories as latent variables and optimizing a weighted likelihood via expectation-maximization, yielding gains on NQ, HotpotQA, MuSiQue, and 2WikiQA.

  5. 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.

  6. ThinkLess: A Training-Free Inference-Efficient Method for Reducing Reasoning Redundancy

    cs.CL 2025-05 conditional novelty 4.0 of 10

    Skipping explicit chain-of-thought reasoning entirely, and prompting for a formatted answer, matches full CoT accuracy on several benchmarks while cutting latency and token counts.

Pith tools