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

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arxiv 2202.01279 v3 pith:XGWJEGHE submitted 2022-02-02 cs.LG cs.CL

Stephen H. Bach , Victor Sanh , Zheng-Xin Yong , Albert Webson , Colin Raffel , Nihal V. Nayak , Abheesht Sharma , Taewoon Kim
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M Saiful Bari Thibault Fevry Zaid Alyafeai Manan Dey Andrea Santilli Zhiqing Sun Srulik Ben-David Canwen Xu Gunjan Chhablani Han Wang Jason Alan Fries Maged S. Al-shaibani Shanya Sharma Urmish Thakker Khalid Almubarak Xiangru Tang Dragomir Radev Mike Tian-Jian Jiang Alexander M. Rush
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classification cs.LGcs.CL
keywords promptspromptsourcelanguagenaturalavailabledevelopmentusersaddresses
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Prompt Orchestration Markup Language

    cs.HC 2025-08 conditional novelty 6.0 of 10

    POML is a markup language that structures LLM prompts, embeds multimodal data, and decouples formatting via stylesheets, with case studies showing strong prompt format sensitivity.

  2. Tensorized Clustered LoRA Merging for Multi-Task Interference

    cs.LG 2025-08 unverdicted novelty 5.0 of 10

    Clustering training data by embedding similarity and jointly CP-decomposing LoRA adapters cuts multi-task merging interference: +1.4% on Phi-3 and +2.3% on Mistral-7B over SVD baselines.

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