Fetching the paper…
Reading the bibliography…
Automatic prompt engineering aims to enhance the generation quality of large language models (LLMs).
Genetic algorithms
John H Holland. 1992 · 1992
Earlier work this paper cites.
Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces
Rainer Storn and Kenneth Price. 1997 · 1997
Earlier work this paper cites.
Autoprompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L Logan IV, Eric Wallace, and Sameer Singh. 2020 · 2010
Earlier work this paper cites.
The winograd schema challenge
Hector Levesque, Ernest Davis, and Leora Morgenstern. 2012 · 2012
Earlier work this paper cites.
Memory: A contribution to experimental psychology
Hermann Ebbinghaus. 2013 · 2013
Earlier work this paper cites.
Creating training corpora for nlg micro-planning
Claire Gardent, Anastasia Shimorina, Shashi Narayan, and Laura Perez-Beltrachini. 2017 · 2017
Earlier work this paper cites.
" liar, liar pants on fire": A new benchmark dataset for fake news detection
William Yang Wang. 2017 · 2017
Earlier work this paper cites.
From arabic sentiment analysis to sarcasm detection: The arsarcasm dataset
Ibrahim Abu Farha and Walid Magdy. 2020 · 2020
Earlier work this paper cites.
Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, and 1 others. 2021 · 2021
Earlier work this paper cites.
The power of scale for parameter-efficient prompt tuning
Brian Lester, Rami Al-Rfou, and Noah Constant. 2021 · 2021
Earlier work this paper cites.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Earlier work this paper cites.
Rlprompt: Optimizing discrete text prompts with reinforcement learning
Mingkai Deng, Jianyu Wang, Cheng-Ping Hsieh, Yihan Wang, Han Guo, Tianmin Shu, Meng Song, Eric P Xing, and Zhiting Hu. 2022 · 2022
Earlier work this paper cites.
Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa. 2022 · 2022
Earlier work this paper cites.
Ethos: a multi-label hate speech detection dataset
Ioannis Mollas, Zoe Chrysopoulou, Stamatis Karlos, and Grigorios Tsoumakas. 2022 · 2022
Earlier work this paper cites.
Chatgpt
OpenAI. 2022 · 2022
Earlier work this paper 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, and 1 others. 2022 · 2022
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 V Le, Ed H Chi, Denny Zhou, and 1 others. 2022 · 2022
Cited alongside, same era.
Glm-130b: An open bilingual pre-trained model
Aohan Zeng, Xiao Liu, Zhengxiao Du, Zihan Wang, Hanyu Lai, Ming Ding, Zhuoyi Yang, Yifan Xu, Wendi Zheng, Xiao Xia, and 1 others. 2022 · 2022
Cited alongside, same era.
Tempera: Test-time prompting via reinforcement learning
Tianjun Zhang, Xuezhi Wang, Denny Zhou, Dale Schuurmans, and Joseph E Gonzalez. 2022 · 2022
Cited alongside, same era.
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
Closest in time.
Optimizing prompts for text-to-image generation
Yaru Hao, Zewen Chi, Li Dong, and Furu Wei. 2024 · 2024
Closest in time.
Task facet learning: A structured approach to prompt optimization
Gurusha Juneja, Nagarajan Natarajan, Hua Li, Jian Jiao, and Amit Sharma. 2024 · 2024
Closest in time.
Promptist: Automated prompt optimization for text-to-image synthesis
WeiJie Li, Jin Wang, and Xuejie Zhang. 2024 · 2024
Closest in time.
Language models as black-box optimizers for vision-language models
Shihong Liu, Samuel Yu, Zhiqiu Lin, Deepak Pathak, and Deva Ramanan. 2024 · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
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.
Agent instructs large language models to be general zero-shot reasoners
Nicholas Crispino, Kyle Montgomery, Fankun Zeng, Dawn Song, and Chenguang Wang. 2023 · 2023
Cited alongside, same era.
Towards an automatic prompt optimization framework for ai image generation
Ling Fan, Harry Jiannan Wang, Kunpeng Zhang, Zilong Pei, and Anjun Li. 2023 · 2023
Cited alongside, same era.
Evoke: Evoking critical thinking abilities in llms via reviewer-author prompt editing
Xinyu Hu, Pengfei Tang, Simiao Zuo, Zihan Wang, Bowen Song, Qiang Lou, Jian Jiao, and Denis Charles. 2023 · 2023
Cited alongside, same era.
Spell: Semantic prompt evolution based on a llm
Yujian Betterest Li and Kai Wu. 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.
Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B Hashimoto. 2023 · 2023
Cited alongside, same era.
Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, and 1 others. 2023 · 2023
Cited alongside, same era.
Ruotian Ma, Xiaolei Wang, Xin Zhou, Jian Li, Nan Du, Tao Gui, Qi Zhang, and Xuanjing Huang. 2024 · 2024
Closest in time.
Improving text-to-image consistency via automatic prompt optimization
Oscar Mañas, Pietro Astolfi, Melissa Hall, Candace Ross, Jack Urbanek, Adina Williams, Aishwarya Agrawal, Adriana Romero-Soriano, and Michal Drozdzal. 2024 · 2024
Closest in time.
Dynamic prompt optimizing for text-to-image generation
Wenyi Mo, Tianyu Zhang, Yalong Bai, Bing Su, Ji-Rong Wen, and Qing Yang. 2024 · 2024
Closest in time.
Xinyu Tang, Xiaolei Wang, Wayne Xin Zhao, Siyuan Lu, Yaliang Li, and Ji-Rong Wen. 2024 · 2024
Closest in time.
Minorityprompt: Text to minority image generation via prompt optimization
Soobin Um and Jong Chul Ye. 2024 · 2024
Closest in time.
Universal prompt optimizer for safe text-to-image generation
Zongyu Wu, Hongcheng Gao, Yueze Wang, Xiang Zhang, and Suhang Wang. 2024 · 2024
Closest in time.
Large language models as optimizers
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, and Xinyun Chen. 2024 · 2024
Closest in time.
Prompt engineering a prompt engineer
Qinyuan Ye, Maxamed Axmed, Reid Pryzant, and Fereshte Khani. 2024 · 2024
Closest in time.
Memorybank: Enhancing large language models with long-term memory
Wanjun Zhong, Lianghong Guo, Qiqi Gao, He Ye, and Yanlin Wang. 2024 · 2024
Closest in time.
Dongsheng Zhu, Daniel Tang, Weidong Han, Jinghui Lu, Yukun Zhao, Guoliang Xing, Junfeng Wang, and Dawei Yin. 2024 · 2024
Closest in time.