mlsanityData to 7 Oct 2026

PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts

arXiv 2202.01279 ↗First posted 2 Feb 2022cs.LG cs.CLcited by 288

Not on the map. Why?

Abstract (arXiv)

PromptSource is a system for creating, sharing, and using natural language prompts. Prompts are functions that map an example from a dataset to a natural language input and target output. Using prompts to train and query language models is an emerging area in NLP that requires new tools that let users develop and refine these prompts collaboratively. PromptSource addresses the emergent challenges in this new setting with (1) a templating language for defining data-linked prompts, (2) an interface that lets users quickly iterate on prompt development by observing outputs of their prompts on many examples, and (3) a community-driven set of guidelines for contributing new prompts to a common pool. Over 2,000 prompts for roughly 170 datasets are already available in PromptSource. PromptSource is available at https://github.com/bigscience-workshop/promptsource.

Key facts. Cited by 288 papers in the map (data through 7 Oct 2026). Builds on 22 of its 37 references that are in the map. First posted on arXiv 2 Feb 2022.

Builds on

22 of 37 references.

References · 22

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  3. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
  4. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  5. 2015A large annotated corpus for learning natural language inferencecited by 1,837
  6. 2021Multitask Prompted Training Enables Zero-Shot Task Generalizationcited by 1,462
  7. 2021Calibrate Before Use: Improving Few-Shot Performance of Language Modelscited by 1,371
  8. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,255
  9. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 934
  10. 2021Cross-Task Generalization via Natural Language Crowdsourcing Instructionscited by 696
  11. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 668
  12. 2021Datasets: A Community Library for Natural Language Processingcited by 526
10 more
  1. 2021MetaICL: Learning to Learn In Contextcited by 450
  2. 2021Dynabench: Rethinking Benchmarking in NLPcited by 447
  3. 2017Zero-Shot Relation Extraction via Reading Comprehensioncited by 425
  4. 2021Learning How to Ask: Querying LMs with Mixtures of Soft Promptscited by 376
  5. 2021True Few-Shot Learning with Language Modelscited by 343
  6. 2021Do Prompt-Based Models Really Understand the Meaning of their Prompts?cited by 324
  7. 2020AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Promptscited by 246
  8. 2021How Many Data Points is a Prompt Worth?cited by 197
  9. 2017YEDDA: A Lightweight Collaborative Text Span Annotation Toolcited by 13
  10. 2020LEAN-LIFE: A Label-Efficient Annotation Framework Towards Learning from Explanationcited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 62; 2023: 113; 2024: 74; 2025: 31; 2026: 8.

288 papers.

Sort:

By area:

  1. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  2. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
  3. 2023A Survey of Large Language Modelscited by 2,537
  4. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,968
  5. 2023ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIscited by 1,880
  6. 2022Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learningcited by 886
  7. 2022Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Taskscited by 858
  8. 2022GLM-130B: An Open Bilingual Pre-trained Modelcited by 837
  9. 2023TIES-Merging: Resolving Interference When Merging Modelscited by 795
  10. 2023LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attentioncited by 776
  11. 2023AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Modelscited by 738
  12. 2023The Flan Collection: Designing Data and Methods for Effective Instruction Tuningcited by 734
38 more
  1. 2023Instruction Tuning with GPT-4cited by 676
  2. 2023Instruction Tuning for Large Language Models: A Surveycited by 617
  3. 2023MotionGPT: Human Motion as a Foreign Languagecited by 521
  4. 2022Unnatural Instructions: Tuning Language Models with (Almost) No Human Laborcited by 395
  5. 2023Scaling Data-Constrained Language Modelscited by 391
  6. 2022DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative Modelscited by 376
  7. 2023LoraHub: Efficient Cross-Task Generalization via Dynamic LoRA Compositioncited by 359
  8. 2023NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmarkcited by 339
  9. 2023Fine-Tuning Language Models with Just Forward Passescited by 337
  10. 2024What matters when building vision-language models?cited by 321
  11. 2023Tool Learning with Foundation Modelscited by 302
  12. 2022Generate rather than Retrieve: Large Language Models are Strong Context Generatorscited by 296
  13. 2024tinyBenchmarks: evaluating LLMs with fewer examplescited by 295
  14. 2023MEGA: Multilingual Evaluation of Generative AIcited by 291
  15. 2022TabLLM: Few-shot Classification of Tabular Data with Large Language Modelscited by 273
  16. 2022What does a platypus look like? Generating customized prompts for zero-shot image classificationcited by 263
  17. 2023GPT4Tools: Teaching Large Language Model to Use Tools via Self-instructioncited by 245
  18. 2022OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalizationcited by 235
  19. 2022Demystifying Prompts in Language Models via Perplexity Estimationcited by 215
  20. 2023EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizerscited by 189
  21. 2024Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuningcited by 174
  22. 2024Lessons from the Trenches on Reproducible Evaluation of Language Modelscited by 172
  23. 2024Building and better understanding vision-language models: insights and future directionscited by 169
  24. 2022Ask Me Anything: A simple strategy for prompting language modelscited by 155
  25. 2023Composing Parameter-Efficient Modules with Arithmetic Operationscited by 155
  26. 2023D4: Improving LLM Pretraining via Document De-Duplication and Diversificationcited by 146
  27. 2022Prompting PaLM for Translation: Assessing Strategies and Performancecited by 144
  28. 2022Efficient Few-Shot Learning Without Promptscited by 135
  29. 2024VidProM: A Million-scale Real Prompt-Gallery Dataset for Text-to-Video Diffusion Modelscited by 124
  30. 2023A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasetscited by 123
  31. 2022Ground-Truth Labels Matter: A Deeper Look into Input-Label Demonstrationscited by 116
  32. 2023FollowBench: A Multi-level Fine-grained Constraints Following Benchmark for Large Language Modelscited by 112
  33. 2022Rationale-Augmented Ensembles in Language Modelscited by 111
  34. 2022Prompting Is Programming: A Query Language for Large Language Modelscited by 110
  35. 2022Task-aware Retrieval with Instructionscited by 107
  36. 2023Instructions as Backdoors: Backdoor Vulnerabilities of Instruction Tuning for Large Language Modelscited by 100
  37. 2022State-of-the-art generalisation research in NLP: A taxonomy and reviewcited by 97
  38. 2025GuardReasoner: Towards Reasoning-based LLM Safeguardscited by 89

50 most cited of 288 All 288 (JSON)