mlsanityData to 7 Oct 2026

HELP ME THINK: A Simple Prompting Strategy for Non-experts to Create Customized Content with Models

arXiv 2208.08232 ↗First posted 17 Aug 2022cs.CL cs.AI cs.CVcited by 17

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

Abstract (arXiv)

Controlling the text generated by language models and customizing the content has been a long-standing challenge. Existing prompting techniques proposed in pursuit of providing control are task-specific and lack generality; this provides overwhelming choices for non-expert users to find a suitable method for their task. The effort associated with those techniques, such as in writing examples, explanations, instructions, etc. further limits their adoption among non-expert users. In this paper, we propose a simple prompting strategy HELP ME THINK where we encourage GPT3 to help non-expert users by asking a set of relevant questions and leveraging user answers to execute the task. We demonstrate the efficacy of our technique HELP ME THINK on a variety of tasks. Specifically, we focus on tasks that are hard for average humans and require significant thinking to perform. We hope our work will encourage the development of unconventional ways to harness the power of large language models.

Key facts. Cited by 17 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 28 of its 37 references that are in the map. First posted on arXiv 17 Aug 2022.

Builds on

28 of 37 references.

Closest ancestors · 3

Same direction, newest first

  1. 2022Least-to-Most Prompting Enables Complex Reasoning in Large Language Models21 May
  2. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Models21 Mar
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Models28 Jan

Other references · 25

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  4. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  5. 2022Do As I Can, Not As I Say: Grounding Language in Robotic Affordancescited by 2,302
  6. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
  7. 2021Show Your Work: Scratchpads for Intermediate Computation with Language Modelscited by 871
  8. 2021Cross-Task Generalization via Natural Language Crowdsourcing Instructionscited by 696
  9. 2022Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priorscited by 487
  10. 2021How Many Data Points is a Prompt Worth?cited by 197
  11. 2021Reframing Instructional Prompts to GPTk's Languagecited by 176
  12. 2021Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt Collectionscited by 128
13 more
  1. 2021A Recipe For Arbitrary Text Style Transfer with Large Language Modelscited by 107
  2. 2020The Turking Test: Can Language Models Understand Instructions?cited by 82
  3. 2020Learning from Task Descriptionscited by 78
  4. 2020Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Modelscited by 65
  5. 2022InstructDial: Improving Zero and Few-shot Generalization in Dialogue through Instruction Tuningcited by 39
  6. 2022Is a Question Decomposition Unit All We Need?cited by 37
  7. 2022InstructionNER: A Multi-Task Instruction-Based Generative Framework for Few-shot NERcited by 31
  8. 2020An Imitation Game for Learning Semantic Parsers from User Interactioncited by 23
  9. 2022Less is More: Summary of Long Instructions is Better for Program Synthesiscited by 17
  10. 2022How Many Data Samples is an Additional Instruction Worth?cited by 16
  11. 2022BioTABQA: Instruction Learning for Biomedical Table Question Answeringcited by 13
  12. 2022Non-Programmers Can Label Programs Indirectly via Active Examples: A Case Study with Text-to-SQLcited by 12
  13. 2022Compositional Task-Oriented Parsing as Abstractive Question Answeringcited by 10

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Citing papers per year: 2022: 2; 2023: 7; 2024: 6; 2025: 2.

17 papers.

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  1. 2023Augmented Language Models: a Surveycited by 398
  2. 2023Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Biascited by 230
  3. 2024Self-Discover: Large Language Models Self-Compose Reasoning Structurescited by 108
  4. 2022Help me write a poem: Instruction Tuning as a Vehicle for Collaborative Poetry Writingcited by 71
  5. 2024LogicBench: Towards Systematic Evaluation of Logical Reasoning Ability of Large Language Modelscited by 67
  6. 2025Feedback Descent: Open-Ended Text Optimization via Pairwise Comparisoncited by 42
  7. 2023Large Language Model Instruction Following: A Survey of Progresses and Challengescited by 31
  8. 2023InstructABSA: Instruction Learning for Aspect Based Sentiment Analysiscited by 22
  9. 2024Healthcare Copilot: Eliciting the Power of General LLMs for Medical Consultationcited by 17
  10. 2022"John is 50 years old, can his son be 65?" Evaluating NLP Models' Understanding of Feasibilitycited by 15
  11. 2024LLM Whisperer: An Inconspicuous Attack to Bias LLM Responsescited by 15
  12. 2023InstructExcel: A Benchmark for Natural Language Instruction in Excelcited by 14
5 more
  1. 2024Chaos with Keywords: Exposing Large Language Models Sycophantic Hallucination to Misleading Keywords and Evaluating Defense Strategiescited by 10
  2. 2023PromptCrafter: Crafting Text-to-Image Prompt through Mixed-Initiative Dialogue with LLMcited by 6
  3. 2023LINGO : Visually Debiasing Natural Language Instructions to Support Task Diversitycited by 0
  4. 2024EventChat: Implementation and user-centric evaluation of a large language model-driven conversational recommender system for exploring leisure events in an SME contextcited by 0
  5. 2025PerHalluEval: Persian Hallucination Evaluation Benchmark for Large Language Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Towards Generalist Prompting for Large Language Models by Mental Models
  2. 2023StrategyLLM: Large Language Models as Strategy Generators, Executors, Optimizers, and Evaluators for Problem Solving
  3. 2024The Prompt Canvas: A Literature-Based Practitioner Guide for Creating Effective Prompts in Large Language Models
  4. 2024A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
  5. 2023Prompting Large Language Models With the Socratic Method
  6. 2022GPT-3-driven pedagogical agents for training children's curious question-asking skills
  7. 2024Multi-expert Prompting Improves Reliability, Safety, and Usefulness of Large Language Models
  8. 2023Prompt Your Mind: Refine Personalized Text Prompts within Your Mind