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In many modern LLM applications, such as retrieval augmented generation, prompts have become programs themselves.
Learning to parse database queries using inductive logic programming
John M Zelle and Raymond J Mooney. 1996 · 1996
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Building a semantic parser overnight
Yushi Wang, Jonathan Berant, and Percy Liang. 2015 · 2015
Earlier work this paper cites.
Regularized evolution for image classifier architecture search
Esteban Real, Alok Aggarwal, Yanping Huang, and Quoc V Le. 2019 · 2019
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Task-oriented dialogue as dataflow synthesis
Jacob Andreas, John Bufe, David Burkett, Charles Chen, Josh Clausman, Jean Crawford, Kate Crim, Jordan DeLoach, Leah Dorner, Jason Eisner, et al. 2020 · 2020
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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 · 2020
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Pyglove: Symbolic programming for automated machine learning
Daiyi Peng, Xuanyi Dong, Esteban Real, Mingxing Tan, Yifeng Lu, Gabriel Bender, Hanxiao Liu, Adam Kraft, Chen Liang, and Quoc Le. 2020 · 2020
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The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
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Learning how to ask: Querying lms with mixtures of soft prompts
Guanghui Qin and Jason Eisner. 2021 · 2021
Earlier work this paper cites.
Super-naturalinstructions:generalization via declarative instructions on 1600+ tasks
Yizhong Wang, Swaroop Mishra, Pegah Alipoormolabashi, Yeganeh Kordi, Amirreza Mirzaei, Anjana Arunkumar, Arjun Ashok, Arut Selvan Dhanasekaran, Atharva Naik, David Stap, et al. 2022 · 2022
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Prompt compression and contrastive conditioning for controllability and toxicity reduction in language models
David Wingate, Mohammad Shoeybi, and Taylor Sorensen. 2022 · 2022
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Leveraging code to improve in-context learning for semantic parsing
Ben Bogin, Shivanshu Gupta, Peter Clark, and Ashish Sabharwal. 2023 · 2023
Cited alongside, same era.
Instructzero: Efficient instruction optimization for black-box large language models
Lichang Chen, Jiuhai Chen, Tom Goldstein, Heng Huang, and Tianyi Zhou. 2023 · 2023
Cited alongside, same era.
Batch prompting: Efficient inference with large language model apis
Zhoujun Cheng, Jungo Kasai, and Tao Yu. 2023 · 2023
Cited alongside, same era.
Promptbreeder: Self-referential self-improvement via prompt evolution
Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, and Tim Rocktäschel. 2023 · 2023
Cited alongside, same era.
GrIPS: Gradient-free, edit-based instruction search for prompting large language models
Archiki Prasad, Peter Hase, Xiang Zhou, and Mohit Bansal. 2023 · 2023
Later among the works it cites.
Automatic prompt optimization with "gradient descent" and beam search
Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, and Michael Zeng. 2023 · 2023
Later among the works it cites.
Melanie Sclar, Yejin Choi, Yulia Tsvetkov, and Alane Suhr. 2023 · 2023
Later among the works it cites.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, et al. 2023 · 2023
Later among the works it cites.
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Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, Yuqing Yang, and Lili Qiu. 2023 · 2023
Cited alongside, same era.
Discrete prompt compression with reinforcement learning
Hoyoun Jung and Kyung-Joong Kim. 2023 · 2023
Cited alongside, same era.
Dspy: Compiling declarative language model calls into self-improving pipelines
Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, Zhiyuan Zhang, Keshav Santhanam, Sri Vardhamanan, Saiful Haq, Ashutosh Sharma, Thomas T. Joshi, Hanna Moazam, Heather Miller, Matei Zaharia, and Christopher Potts. 2023 · 2023
Cited alongside, same era.
Compressing context to enhance inference efficiency of large language models
Yucheng Li, Bo Dong, Frank Guerin, and Chenghua Lin. 2023 · 2023
Cited alongside, same era.
Batchprompt: Accomplish more with less
Jianzhe Lin, Maurice Diesendruck, Liang Du, and Robin Abraham. 2023 · 2023
Cited alongside, same era.
Learning to compress prompts with gist tokens
Jesse Mu, Xiang Lisa Li, and Noah Goodman. 2023 · 2023
Cited alongside, same era.
Did you read the instructions? Rethinking the effectiveness of task definitions in instruction learning
Fan Yin, Jesse Vig, Philippe Laban, Shafiq Joty, Caiming Xiong, and Chien-Sheng Wu. 2023a
Cited in the paper.
Fan Yin, Jesse Vig, Philippe Laban, Shafiq Joty, Caiming Xiong, and Chien-Sheng Jason Wu. 2023b
Cited in the paper.
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
Later among the works it cites.
Prompt engineering a prompt engineer
Qinyuan Ye, Maxamed Axmed, Reid Pryzant, and Fereshte Khani. 2023 · 2023
Later among the works it cites.
Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2023 · 2023
Later among the works it cites.
Albert Q Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, et al. 2024 · 2024
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Lost in the middle: How language models use long contexts
Nelson F Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang. 2024 · 2024
Closest in time.