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Large Language Models (LLMs) have gained widespread popularity due to their ability to perform ad-hoc Natural Language Processing (NLP) tasks with a simple natural language prompt.
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Grounding interactive machine learning tool design in how non-experts actually build models
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Language models are few-shot learners
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Attention flows: Analyzing and comparing attention mechanisms in language models
J. F. DeRose, J. Wang, and M. Berger · 2020
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Cross-task generalization via natural language crowdsourcing instructions
S. Mishra, D. Khashabi, C. Baral, and H. Hajishirzi · 2021
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L. Reynolds and K. McDonell · 2021
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Detecting formal thought disorder by deep contextualized word representations
J. Sarzynska-Wawer, A. Wawer, A. Pawlak, J. Szymanowska, I. Stefaniak, M. Jarkiewicz, and L. Okruszek · 2021
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How many data points is a prompt worth?
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Z. J. Wang, R. Turko, and D. H. Chau · 2021
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Openprompt: An open-source framework for prompt-learning
N. Ding, S. Hu, W. Zhao, Y. Chen, Z. Liu, H.-T. Zheng, and M. Sun · 2021
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Visqa: X-raying vision and language reasoning in transformers
T. Jaunet, C. Kervadec, R. Vuillemot, G. Antipov, M. Baccouche, and C. Wolf · 2021
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Promptsource: An integrated development environment and repository for natural language prompts
S. H. Bach, V. Sanh, Z.-X. Yong, A. Webson, C. Raffel, N. V. Nayak, A. Sharma, T. Kim, M. S. Bari, T. Fevry, et al · 2022
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Promptmaker: Prompt-based prototyping with large language models
E. Jiang, K. Olson, E. Toh, A. Molina, A. Donsbach, M. Terry, and C. J. Cai · 2022
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Help me think: A simple prompting strategy for non-experts to create customized content with models
S. Mishra and E. Nouri · 2022
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Diversity vs relevance: A practical multi-objective study in luxury fashion recommendations
J. Sá, V. Queiroz Marinho, A. R. Magalhães, T. Lacerda, and D. Goncalves · 2022
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Interactive and visual prompt engineering for ad-hoc task adaptation with large language models
H. Strobelt, A. Webson, V. Sanh, B. Hoover, J. Beyer, H. Pfister, and A. M. Rush · 2022
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Diffusiondb: A large-scale prompt gallery dataset for text-to-image generative models
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Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig · 2023
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Why johnny can’t prompt: How non-ai experts try (and fail) to design llm prompts
J. Zamfirescu-Pereira, R. Wong, B. Hartmann, and Q. Yang · 2023
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