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Prompt optimization is essential for effective utilization of large language models (LLMs) across diverse tasks.
Instruction induction: From few examples to natural language task descriptions
Or Honovich, Uri Shaham, Samuel R. Bowman, and Omer Levy. 2023 · 1952
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Language models are few-shot learners
Tom B Brown. 2020 · 2005
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Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych. 2019 · 2019
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GoEmotions: A Dataset of Fine-Grained Emotions
Dorottya Demszky, Dana Movshovitz-Attias, Jeongwoo Ko, Alan Cowen, Gaurav Nemade, and Sujith Ravi. 2020 · 2020
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How can we know what language models know?
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 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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Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
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Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Z Yang, and J Tang. 2021 · 2021
Earlier work this paper cites.
Learning how to ask: Querying lms with mixtures of soft prompts
Guanghui Qin and Jason Eisner. 2021 · 2021
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Prompt programming for large language models: Beyond the few-shot paradigm
Laria Reynolds and Kyle McDonell. 2021 · 2021
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Calibrate before use: Improving few-shot performance of language models
Zihao Zhao, Eric Wallace, Shi Feng, Dan Klein, and Sameer Singh. 2021 · 2021
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Promptmaker: Prompt-based prototyping with large language models
Ellen Jiang, Kristen Olson, Edwin Toh, Alejandra Molina, Aaron Donsbach, Michael Terry, and Carrie J Cai. 2022 · 2022
Earlier work this paper cites.
Fantastically ordered prompts and where to find them: Overcoming few-shot prompt order sensitivity
Yao Lu, Max Bartolo, Alastair Moore, Sebastian Riedel, and Pontus Stenetorp. 2022 · 2022
Earlier work this paper cites.
Grips: Gradient-free, edit-based instruction search for prompting large language models
Archiki Prasad, Peter Hase, Xiang Zhou, and Mohit Bansal. 2022 · 2022
Earlier work this paper cites.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc V Le, Ed H Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou. 2022 · 2022
Cited alongside, same era.
Gps: Genetic prompt search for efficient few-shot learning
Hanwei Xu, Yujun Chen, Yulun Du, Nan Shao, Yanggang Wang, Haiyu Li, and Zhilin Yang. 2022 · 2022
Cited alongside, same era.
Large language models are human-level prompt engineers
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba. 2022 · 2022
Cited alongside, same era.
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. 2023 · 2023
Cited alongside, same era.
Sparks of artificial general intelligence: Early experiments with gpt-4
Efficient prompting methods for large language models: A survey
Kaiyan Chang, Songcheng Xu, Chenglong Wang, Yingfeng Luo, Tong Xiao, and Jingbo Zhu. 2024 · 2024
Closest in time.
PACE: Improving prompt with actor-critic editing for large language model
Yihong Dong, Kangcheng Luo, Xue Jiang, Zhi Jin, and Ge Li. 2024 · 2024
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Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang, Angela Fan, et al. 2024 · 2024
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Promptbreeder: Self-referential self-improvement via prompt evolution
Chrisantha Fernando, Dylan Sunil Banarse, Henryk Michalewski, Simon Osindero, and Tim Rocktäschel. 2024 · 2024
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Connecting large language models with evolutionary algorithms yields powerful prompt optimizers
Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, and Yujiu Yang. 2024 · 2024
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Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023 · 2023
Cited alongside, same era.
Black-box prompt optimization: Aligning large language models without model training
Jiale Cheng, Xiao Liu, Kehan Zheng, Pei Ke, Hongning Wang, Yuxiao Dong, Jie Tang, and Minlie Huang. 2023 · 2023
Cited alongside, same era.
Beavertails: Towards improved safety alignment of llm via a human-preference dataset
Jiaming Ji, Mickel Liu, Josef Dai, Xuehai Pan, Chi Zhang, Ce Bian, Boyuan Chen, Ruiyang Sun, Yizhou Wang, and Yaodong Yang. 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, et al. 2023 · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Autohint: Automatic prompt optimization with hint generation
Hong Sun, Xue Li, Yinchuan Xu, Youkow Homma, Qi Cao, Min Wu, Jian Jiao, and Denis Charles. 2023 · 2023
Cited alongside, same era.
Challenging BIG-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc Le, Ed Chi, Denny Zhou, and Jason Wei. 2023 · 2023
Cited alongside, same era.
Prompt engineering a prompt engineer
Qinyuan Ye, Maxamed Axmed, Reid Pryzant, and Fereshte Khani. 2023 · 2023
Cited alongside, same era.
Closest in time.
Automatic engineering of long prompts
Cho-Jui Hsieh, Si Si, Felix Yu, and Inderjit Dhillon. 2024 · 2024
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Large language models as evolutionary optimizers
Shengcai Liu, Caishun Chen, Xinghua Qu, Ke Tang, and Yew-Soon Ong. 2024 · 2024
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Are large language models good prompt optimizers?
Ruotian Ma, Xiaolei Wang, Xin Zhou, Jian Li, Nan Du, Tao Gui, Qi Zhang, and Xuanjing Huang. 2024 · 2024
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Introducing gpt-4o
OpenAI. 2024 · 2024
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A systematic survey of prompt engineering in large language models: Techniques and applications
Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, and Aman Chadha. 2024 · 2024
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Prompts as programs: A structure-aware approach to efficient compile-time prompt optimization
Tobias Schnabel and Jennifer Neville. 2024 · 2024
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Large language models as optimizers
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V Le, Denny Zhou, and Xinyun Chen. 2024 · 2024
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Textgrad: Automatic" differentiation" via text
Mert Yuksekgonul, Federico Bianchi, Joseph Boen, Sheng Liu, Zhi Huang, Carlos Guestrin, and James Zou. 2024 · 2024
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