mlsanityData to 14 Oct 2026

Reprompting: Automated Chain-of-Thought Prompt Inference Through Gibbs Sampling

arXiv 2305.09993 ↗First posted 17 May 2023cs.LG cs.AI cs.CLcited by 47

On the map: LLM reasoning → Prompting and chain-of-thought reasoning

Abstract (arXiv)

We introduce Reprompting, an iterative sampling algorithm that automatically learns the Chain-of-Thought (CoT) recipes for a given task without human intervention. Through Gibbs sampling, Reprompting infers the CoT recipes that work consistently well for a set of training samples by iteratively sampling new recipes using previously sampled recipes as parent prompts to solve other training problems. We conduct extensive experiments on 20 challenging reasoning tasks. Results show that Reprompting outperforms human-written CoT prompts substantially by +9.4 points on average. It also achieves consistently better performance than the state-of-the-art prompt optimization and decoding algorithms.

Key facts. Cited by 47 papers in the map (data through 14 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 26 of its 40 references that are in the map. First posted on arXiv 17 May 2023.

Builds on

26 of 40 references.

Closest ancestors · 8

Same direction, newest first

  1. 2023Progressive-Hint Prompting Improves Reasoning in Large Language Models19 Apr
  2. 2022Challenging BIG-Bench Tasks and Whether Chain-of-Thought Can Solve Them17 Oct
  3. 2022Automatic Chain of Thought Prompting in Large Language Models7 Oct
  4. 2022Complexity-Based Prompting for Multi-Step Reasoning3 Oct
  5. 2022Text and Patterns: For Effective Chain of Thought, It Takes Two to Tango16 Sep
  6. 2022Large Language Models are Zero-Shot Reasoners24 May
  7. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Models21 May
  8. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Models21 Mar

Other references · 18

  1. 2022Training language models to follow instructions with human feedbackcited by 15,971
  2. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  3. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  4. 2021Measuring Mathematical Problem Solving With the MATH Datasetcited by 5,371
  5. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  6. 2021Program Synthesis with Large Language Modelscited by 3,637
  7. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,998
  8. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
  9. 2022STaR: Bootstrapping Reasoning With Reasoningcited by 940
  10. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 938
  11. 2021Show Your Work: Scratchpads for Intermediate Computation with Language Modelscited by 886
  12. 2022RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learningcited by 366
6 more
  1. 2022Selection-Inference: Exploiting Large Language Models for Interpretable Logical Reasoningcited by 334
  2. 2022Making Large Language Models Better Reasoners with Step-Aware Verifiercited by 233
  3. 2023ART: Automatic multi-step reasoning and tool-use for large language modelscited by 197
  4. 2022Ask Me Anything: A simple strategy for prompting language modelscited by 155
  5. 2022Rationale-Augmented Ensembles in Language Modelscited by 111
  6. 2023Answering Questions by Meta-Reasoning over Multiple Chains of Thoughtcited by 87

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 12; 2024: 12; 2025: 13; 2026: 10.

47 papers.

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  1. 2023Orca: Progressive Learning from Complex Explanation Traces of GPT-4cited by 379
  2. 2024Evolution of Heuristics: Towards Efficient Automatic Algorithm Design Using Large Language Modelcited by 268
  3. 2023PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt Optimizationcited by 233
  4. 2023Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Futurecited by 184
  5. 2023Amortizing intractable inference in large language modelscited by 97
  6. 2023Unleashing the potential of prompt engineering for large language modelscited by 72
  7. 2025A Systematic Survey of Automatic Prompt Optimization Techniquescited by 63
  8. 2024Efficient Prompting Methods for Large Language Models: A Surveycited by 47
  9. 2023Joint Prompt Optimization of Stacked LLMs using Variational Inferencecited by 32
  10. 2023AutoHint: Automatic Prompt Optimization with Hint Generationcited by 30
  11. 2023Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulationcited by 30
  12. 2023Supervised Knowledge Makes Large Language Models Better In-context Learnerscited by 20
35 more
  1. 2024Preemptive Answer "Attacks" on Chain-of-Thought Reasoningcited by 19
  2. 2024Autonomous Industrial Control using an Agentic Framework with Large Language Modelscited by 19
  3. 2024Can We Verify Step by Step for Incorrect Answer Detection?cited by 15
  4. 2025MARS: Multi-Agent Adaptive Reasoning with Socratic Guidance for Automated Prompt Optimizationcited by 15
  5. 2023StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solvingcited by 13
  6. 2024Distilling Reasoning Ability from Large Language Models with Adaptive Thinkingcited by 13
  7. 2024QPO: Query-dependent Prompt Optimization via Multi-Loop Offline Reinforcement Learningcited by 10
  8. 2025Autonomous Control Leveraging LLMs: An Agentic Framework for Next-Generation Industrial Automationcited by 8
  9. 2024Integrating Planning into Single-Turn Long-Form Text Generationcited by 7
  10. 2025RMoA: Optimizing Mixture-of-Agents through Diversity Maximization and Residual Compensationcited by 7
  11. 2025No Loss, No Gain: Gated Refinement and Adaptive Compression for Prompt Optimizationcited by 7
  12. 2025Large (Vision) Language Models are Unsupervised In-Context Learnerscited by 6
  13. 2025Revisiting Prompt Optimization with Large Reasoning Models-A Case Study on Event Extractioncited by 6
  14. 2026Towards Self-Robust LLMs: Intrinsic Prompt Noise Resistance via CoIPOcited by 5
  15. 2023Domain-specific ChatBots for Science using Embeddingscited by 4
  16. 2024Logicbreaks: A Framework for Understanding Subversion of Rule-based Inferencecited by 4
  17. 2025A Survey of Deep Learning for Geometry Problem Solvingcited by 4
  18. 2024Towards a Science Exocortexcited by 3
  19. 2023Do Physicians Know How to Prompt? The Need for Automatic Prompt Optimization Help in Clinical Note Generationcited by 2
  20. 2024Instruction Makes a Differencecited by 2
  21. 2025Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agentscited by 2
  22. 2025City Navigation in the Wild: Exploring Emergent Navigation from Web-Scale Knowledge in MLLMscited by 1
  23. 2026Self-Improvement as Coherence Optimization: A Theoretical Accountcited by 1
  24. 2026Helix: A Dual-Helix Co-Evolutionary Multi-Agent System for Prompt Optimization and Question Reformulationcited by 1
  25. 2024fLSA: Learning Semantic Structures in Document Collections Using Foundation Modelscited by 0
  26. 2025Working Document -- Formalising Software Requirements with Large Language Modelscited by 0
  27. 2025Dr. Boot: Bootstrapping Program Synthesis Language Models to Perform Repairingcited by 0
  28. 2025Leveraging Large Language Models for Cybersecurity Risk Assessment -- A Case from Forestry Cyber-Physical Systemscited by 0
  29. 2026Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinementcited by 0
  30. 2026RosettaSearch: Multi-Objective Inference-Time Search for Protein Sequence Designcited by 0
  31. 2026Structured Inference with Large Language Gibbscited by 0
  32. 2026Towards Spec Learning: Inference-Time Alignment from Preference Pairscited by 0
  33. 2026BayesPO: Bayesian Prompt Optimization via Parallel-Tempered Gradient-Guided Discrete MCMCcited by 0
  34. 2026StraTune: Adaptive Selection of Revision Operators for Self-Evolving LLM Skillscited by 0
  35. 2026Reasoning for Propaganda Detection via Label-Guided CoT Optimisationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods
  2. 2023Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Models
  3. 2025Fractured Chain-of-Thought Reasoning
  4. 2025Enhancing Auto-regressive Chain-of-Thought through Loop-Aligned Reasoning
  5. 2022Iteratively Prompt Pre-trained Language Models for Chain of Thought
  6. 2023Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models
  7. 2025SCOUT: Teaching Pre-trained Language Models to Enhance Reasoning via Flow Chain-of-Thought
  8. 2025RePrompt: Reasoning-Augmented Reprompting for Text-to-Image Generation via Reinforcement Learning