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Interaction with Large Language Models (LLMs) is primarily carried out via prompting.
Beyond accuracy: The role of mental models in human-ai team performance
Gagan Bansal, Besmira Nushi, Ece Kamar, Walter S Lasecki, Daniel S Weld, and Eric Horvitz · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Mental models of ai agents in a cooperative game setting
Katy Ilonka Gero, Zahra Ashktorab, Casey Dugan, Qian Pan, James Johnson, Werner Geyer, Maria Ruiz, Sarah Miller, David R Millen, Murray Campbell, et al · 2020
Earlier work this paper cites.
Promptsource: An integrated development environment and repository for natural language prompts
Stephen H Bach, Victor Sanh, Zheng-Xin Yong, Albert Webson, Colin Raffel, Nihal V Nayak, Abheesht Sharma, Taewoon Kim, M Saiful Bari, Thibault Fevry, et al · 2022
Earlier work this paper cites.
Hai Dang, Lukas Mecke, Florian Lehmann, Sven Goller, and Daniel Buschek · 2022
Earlier work this paper cites.
Discovering the syntax and strategies of natural language programming with generative language models
Ellen Jiang, Edwin Toh, Alejandra Molina, Kristen Olson, Claire Kayacik, Aaron Donsbach, Carrie J Cai, and Michael Terry · 2022
Earlier work this paper cites.
Interactive and visual prompt engineering for ad-hoc task adaptation with large language models
Hendrik Strobelt, Albert Webson, Victor Sanh, Benjamin Hoover, Johanna Beyer, Hanspeter Pfister, and Alexander M Rush · 2022
Earlier work this paper cites.
Investigating explainability of generative ai for code through scenario-based design
Jiao Sun, Q Vera Liao, Michael Muller, Mayank Agarwal, Stephanie Houde, Kartik Talamadupula, and Justin D Weisz · 2022
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Earlier work this paper cites.
Promptchainer: Chaining large language model prompts through visual programming
Tongshuang Wu, Ellen Jiang, Aaron Donsbach, Jeff Gray, Alejandra Molina, Michael Terry, and Carrie J Cai · 2022
Earlier work this paper cites.
Chainforge: An open-source visual programming environment for prompt engineering
Ian Arawjo, Priyan Vaithilingam, Martin Wattenberg, and Elena Glassman · 2023
Cited alongside, same era.
The foundation model transparency index
Rishi Bommasani, Kevin Klyman, Shayne Longpre, Sayash Kapoor, Nestor Maslej, Betty Xiong, Daniel Zhang, and Percy Liang · 2023
Cited alongside, same era.
Early llm-based tools for enterprise information workers likely provide meaningful boosts to productivity
Alexia Cambon, Brent Hecht, Ben Edelman, Donald Ngwe, Sonia Jaffe, Amy Heger, Mihaela Vorvoreanu, Sida Peng, Jake Hofman, Alex Farach, et al · 2023
Cited alongside, same era.
Yoonsu Kim, Jueon Lee, Seoyoung Kim, Jaehyuk Park, and Juho Kim · 2023
Cited alongside, same era.
Ai transparency in the age of llms: A human-centered research roadmap
Transforming boundaries: how does chatgpt change knowledge work?
Paavo Ritala, Mika Ruokonen, and Laavanya Ramaul · 2023
Later among the works it cites.
Cataloging prompt patterns to enhance the discipline of prompt engineering
Douglas C Schmidt, Jesse Spencer-Smith, Quchen Fu, and Jules White · 2023
Later among the works it cites.
Promptagent: Strategic planning with language models enables expert-level prompt optimization
Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai, Haotian Luo, Jiayou Zhang, Nebojsa Jojic, Eric P Xing, and Zhiting Hu · 2023
Later among the works it cites.
A prompt pattern catalog to enhance prompt engineering with chatgpt
Jules White, Quchen Fu, Sam Hays, Michael Sandborn, Carlos Olea, Henry Gilbert, Ashraf Elnashar, Jesse Spencer-Smith, and Douglas C Schmidt · 2023
Later among the works it cites.
Why johnny can’t prompt: how non-ai experts try (and fail) to design llm prompts
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Q Vera Liao and Jennifer Wortman Vaughan · 2023
Cited alongside, same era.
Ten simple rules for crafting effective prompts for large language models
Zhicheng Lin · 2023
Cited alongside, same era.
“what it wants me to say”: Bridging the abstraction gap between end-user programmers and code-generating large language models
Michael Xieyang Liu, Advait Sarkar, Carina Negreanu, Benjamin Zorn, Jack Williams, Neil Toronto, and Andrew D Gordon · 2023
Cited alongside, same era.
Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig · 2023
Cited alongside, same era.
Propane: Prompt design as an inverse problem
Rimon Melamed, Lucas H McCabe, Tanay Wakhare, Yejin Kim, H Howie Huang, and Enric Boix-Adsera · 2023
Cited alongside, same era.
Aditi Mishra, Utkarsh Soni, Anjana Arunkumar, Jinbin Huang, Bum Chul Kwon, and Chris Bryan · 2023
Cited alongside, same era.
JD Zamfirescu-Pereira, Richmond Y Wong, Bjoern Hartmann, and Qian Yang · 2023
Later among the works it cites.
User intent recognition and satisfaction with large language models: A user study with chatgpt
Anna Bodonhelyi, Efe Bozkir, Shuo Yang, Enkelejda Kasneci, and Gjergji Kasneci · 2024
Closest in time.
Can (a) i have a word with you? a taxonomy on the design dimensions of ai prompts
Marvin Braun, Maike Greve, Felix Kegel, Lutz Kolbe, and Philipp Emanuel Beyer · 2024
Closest in time.
Guiding large language models via directional stimulus prompting
Zekun Li, Baolin Peng, Pengcheng He, Michel Galley, Jianfeng Gao, and Xifeng Yan · 2024
Closest in time.
difflib: Helpers for Computing String Similarities and Differences
Python Software Foundation · 2024
Closest in time.