mlsanityData to 7 Oct 2026

Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models

arXiv 2302.00618 ↗First posted 1 Feb 2023cs.CLcited by 61

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

Abstract (arXiv)

Large language models can perform various reasoning tasks by using chain-of-thought prompting, which guides them to find answers through step-by-step demonstrations. However, the quality of the prompts depends on the demonstrations given to the models, and creating many of them by hand is costly. We introduce Synthetic prompting, a method that leverages a few handcrafted examples to prompt the model to generate more examples by itself, and selects effective demonstrations to elicit better reasoning. Our method alternates between a backward and forward process to generate new examples. The backward process generates a question that match a sampled reasoning chain, so that the question is solvable and clear. The forward process produces a more detailed reasoning chain for the question, improving the quality of the example. We evaluate our method on numerical, symbolic, and algorithmic reasoning tasks, and show that it outperforms existing prompting techniques.

Key facts. Cited by 61 papers in the map (data through 7 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 35 of its 41 references that are in the map. First posted on arXiv 1 Feb 2023.

Builds on

35 of 41 references.

Closest ancestors · 8

Same direction, newest first

  1. 2022Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks22 Nov
  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. 2022Large Language Models are Zero-Shot Reasoners24 May
  6. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Models21 May
  7. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Models21 Mar
  8. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models28 Jan

Other references · 27

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2021Training Verifiers to Solve Math Word Problemscited by 9,743
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  5. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  6. 2022OPT: Open Pre-trained Transformer Language Modelscited by 3,464
  7. 2022Scaling Instruction-Finetuned Language Modelscited by 2,997
  8. 2019The Curious Case of Neural Text Degenerationcited by 2,881
  9. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
  10. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  11. 2021What Makes Good In-Context Examples for GPT-3?cited by 1,184
  12. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,054
15 more
  1. 2021Are NLP Models really able to Solve Simple Math Word Problems?cited by 861
  2. 2022Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Taskscited by 858
  3. 2022Large Language Models Can Self-Improvecited by 656
  4. 2022PAL: Program-aided Language Modelscited by 635
  5. 2022Decomposed Prompting: A Modular Approach for Solving Complex Taskscited by 554
  6. 2021A Diverse Corpus for Evaluating and Developing English Math Word Problem Solverscited by 314
  7. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 286
  8. 2021Symbolic Knowledge Distillation: from General Language Models to Commonsense Modelscited by 270
  9. 2022ZeroGen: Efficient Zero-shot Learning via Dataset Generationcited by 209
  10. 2022WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creationcited by 200
  11. 2022Compositional Semantic Parsing with Large Language Modelscited by 95
  12. 2022On the Effect of Pretraining Corpora on In-context Learning by a Large-scale Language Modelcited by 78
  13. 2022Complementary Explanations for Effective In-Context Learningcited by 71
  14. 2022Z-ICL: Zero-Shot In-Context Learning with Pseudo-Demonstrationscited by 70
  15. 2022Reasoning Like Program Executorscited by 45

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 25; 2024: 21; 2025: 13; 2026: 2.

61 papers.

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  1. 2023A Survey of Large Language Modelscited by 2,537
  2. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 803
  3. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 343
  4. 2023Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervisioncited by 318
  5. 2023ToRA: A Tool-Integrated Reasoning Agent for Mathematical Problem Solvingcited by 276
  6. 2023Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Biascited by 230
  7. 2024The Impact of Reasoning Step Length on Large Language Modelscited by 152
  8. 2023Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselvescited by 123
  9. 2023Algorithm of Thoughts: Enhancing Exploration of Ideas in Large Language Modelscited by 92
  10. 2023Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extractioncited by 89
  11. 202314 Examples of How LLMs Can Transform Materials Science and Chemistry: A Reflection on a Large Language Model Hackathoncited by 85
  12. 2023Exposing Attention Glitches with Flip-Flop Language Modelingcited by 74
38 more
  1. 2024The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harmcited by 67
  2. 2023Towards Better Chain-of-Thought Prompting Strategies: A Surveycited by 62
  3. 2024TextSquare: Scaling up Text-Centric Visual Instruction Tuningcited by 62
  4. 2024Key-Point-Driven Data Synthesis with its Enhancement on Mathematical Reasoningcited by 58
  5. 2024Unlocking the Capabilities of Thought: A Reasoning Boundary Framework to Quantify and Optimize Chain-of-Thoughtcited by 56
  6. 2024A Survey of Prompt Engineering Methods in Large Language Models for Different NLP Taskscited by 46
  7. 2024Evolving Code with A Large Language Modelcited by 42
  8. 2025Step-GUI Technical Reportcited by 38
  9. 2023MoT: Memory-of-Thought Enables ChatGPT to Self-Improvecited by 37
  10. 2024Do LLMs Understand Visual Anomalies? Uncovering LLM's Capabilities in Zero-shot Anomaly Detectioncited by 35
  11. 2023Let GPT be a Math Tutor: Teaching Math Word Problem Solvers with Customized Exercise Generationcited by 27
  12. 2023Pushing the Limits of ChatGPT on NLP Taskscited by 27
  13. 2023PsyCoT: Psychological Questionnaire as Powerful Chain-of-Thought for Personality Detectioncited by 21
  14. 2023Enhancing Chain-of-Thoughts Prompting with Iterative Bootstrapping in Large Language Modelscited by 16
  15. 2023LLMs Killed the Script Kiddie: How Agents Supported by Large Language Models Change the Landscape of Network Threat Testingcited by 16
  16. 2023Social Learning: Towards Collaborative Learning with Large Language Modelscited by 15
  17. 2024Enhancing Chain of Thought Prompting in Large Language Models via Reasoning Patternscited by 13
  18. 2024Efficient Prompting for LLM-based Generative Internet of Thingscited by 12
  19. 2023Prompt Space Optimizing Few-shot Reasoning Success with Large Language Modelscited by 11
  20. 2024Towards Hierarchical Multi-Agent Workflows for Zero-Shot Prompt Optimizationcited by 10
  21. 2024Can LLMs get help from other LLMs without revealing private information?cited by 9
  22. 2024Boosting Reward Model with Preference-Conditional Multi-Aspect Synthetic Data Generationcited by 9
  23. 2024Enhancing Robustness in Large Language Models: Prompting for Mitigating the Impact of Irrelevant Informationcited by 8
  24. 2023Self-Convinced Prompting: Few-Shot Question Answering with Repeated Introspectioncited by 7
  25. 2024Enhancing Large Language Model with Decomposed Reasoning for Emotion Cause Pair Extractioncited by 7
  26. 2023WALL-E: Embodied Robotic WAiter Load Lifting with Large Language Modelcited by 6
  27. 2023Bridging Code Semantic and LLMs: Semantic Chain-of-Thought Prompting for Code Generationcited by 6
  28. 2024Mitigating Sycophancy in Decoder-Only Transformer Architectures: Synthetic Data Interventioncited by 5
  29. 2025Unified Mind Model: Reimagining Autonomous Agents in the LLM Eracited by 5
  30. 2025SHARP: Synthesizing High-quality Aligned Reasoning Problems for Large Reasoning Models Reinforcement Learningcited by 5
  31. 2023Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learningcited by 4
  32. 2025Electronic Circuit Principles of Large Language Modelscited by 4
  33. 2026Reasoning-Driven Synthetic Data Generation and Evaluationcited by 4
  34. 2025Gricean Norms as a Basis for Effective Collaborationcited by 3
  35. 2025Cognitive Chain-of-Thought (CoCoT): Structured Multimodal Reasoning about Social Situationscited by 3
  36. 2024Look Before You Leap: Problem Elaboration Prompting Improves Mathematical Reasoning in Large Language Modelscited by 2
  37. 2024Data Augmentation of Multi-turn Psychological Dialogue via Knowledge-driven Progressive Thought Promptingcited by 2
  38. 2025Dynamics of Spontaneous Topic Changes in Next Token Prediction with Self-Attentioncited by 2

50 most cited of 61 All 61 (JSON)

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  3. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models
  4. 2022Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought
  5. 2024Unveiling the Statistical Foundations of Chain-of-Thought Prompting Methods
  6. 2024ChainLM: Empowering Large Language Models with Improved Chain-of-Thought Prompting
  7. 2023Why Can Large Language Models Generate Correct Chain-of-Thoughts?
  8. 2024Self-Harmonized Chain of Thought