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Since the emergence of large language models, prompt learning has become a popular method for optimizing and customizing these models.
Optimization by simulated annealing
Scott Kirkpatrick, C Daniel Gelatt Jr, and Mario P Vecchi · 1983
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Future paths for integer programming and links to artificial intelligence
Fred Glover · 1986
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Genetic algorithms
John H Holland · 1992
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Simulated annealing: A proof of convergence
Vincent Granville, Mirko Krivánek, and J-P Rasson · 1994
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Particle swarm optimization
James Kennedy and Russell Eberhart · 1995
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Ant colony system: a cooperative learning approach to the traveling salesman problem
Marco Dorigo and Luca Maria Gambardella · 1997
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Convergence of a simulated annealing algorithm for continuous global optimization
Marco Locatelli · 2000
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A new heuristic optimization algorithm: harmony search
Zong Woo Geem, Joong Hoon Kim, and Gobichettipalayam Vasudevan Loganathan · 2001
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Tabu search and finite convergence
Fred Glover and Saıd Hanafi · 2002
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Theory of genetic algorithms ii: models for genetic operators over the string-tensor representation of populations and convergence to global optima for arbitrary fitness function under scaling
Lothar M Schmitt · 2004
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Ant colony optimization
Marco Dorigo, Mauro Birattari, and Thomas Stutzle · 2006
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Cuckoo search via lévy flights
Xin-She Yang and Suash Deb · 2009
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Discrete cuckoo search algorithm for the travelling salesman problem
Aziz Ouaarab, Belaïd Ahiod, and Xin-She Yang · 2014
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MAWPS: A math word problem repository
Rik Koncel-Kedziorski, Subhro Roy, Aida Amini, Nate Kushman, and Hannaneh Hajishirzi · 2016
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Genetic algorithm for traveling salesman problem with modified cycle crossover operator
Abid Hussain, Yousaf Shad Muhammad, M Nauman Sajid, Ijaz Hussain, Alaa Mohamd Shoukry, Showkat Gani, et al · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
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Global convergence analysis of cuckoo search using markov theory
Xing-Shi He, Fan Wang, Yan Wang, and Xin-She Yang · 2018
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Commonsense knowledge mining from pretrained models
Joe Davison, Joshua Feldman, and Alexander Rush · 2019
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Neural architecture search: A survey
Thomas Elsken, Jan Hendrik Metzen, and Frank Hutter · 2019
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The application of simulated annealing method for optimal route detection between objects
Peter Grabusts, Jurijs Musatovs, and Vladimir Golenkov · 2019
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Metaheuristic research: a comprehensive survey
Kashif Hussain, Mohd Najib Mohd Salleh, Shi Cheng, and Yuhui Shi · 2019
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Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
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CommonsenseQA: A question answering challenge targeting commonsense knowledge
Alon Talmor, Jonathan Herzig, Nicholas Lourie, and Jonathan Berant · 2019
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Universal adversarial triggers for attacking and analyzing NLP
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh · 2019
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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
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How can we know what language models know?
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig · 2020
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A diverse corpus for evaluating and developing English math word problem solvers
Shen-yun Miao, Chao-Chun Liang, and Keh-Yih Su · 2020
Cited alongside, same era.
AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh · 2020
Cited alongside, same era.
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, Christopher Hesse, and John Schulman · 2021
Cited alongside, same era.
Making pre-trained language models better few-shot learners
Tianyu Gao, Adam Fisch, and Danqi Chen · 2021
Cited alongside, same era.
Did Aristotle Use a Laptop? A Question Answering Benchmark with Implicit Reasoning Strategies
Mor Geva, Daniel Khashabi, Elad Segal, Tushar Khot, Dan Roth, and Jonathan Berant · 2021
Cited alongside, same era.
Bbtv2: Towards a gradient-free future with large language models
Tianxiang Sun, Zhengfu He, Hong Qian, Yunhua Zhou, Xuan-Jing Huang, and Xipeng Qiu · 2022
Later among the works it cites.
Self-consistency improves chain of thought reasoning in language models
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, and Denny Zhou · 2022
Later among the works it cites.
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
Later among the works it cites.
Automatic chain of thought prompting in large language models, 2022
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola · 2022
Later among the works it cites.
Llemma: An open language model for mathematics
Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, Marco Dos Santos, Stephen McAleer, Albert Q Jiang, Jia Deng, Stella Biderman, and Sean Welleck · 2023
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WARP: Word-level Adversarial ReProgramming
Karen Hambardzumyan, Hrant Khachatrian, and Jonathan May · 2021
Cited alongside, same era.
PTR: Prompt Tuning with Rules for Text Classification
Xu Han, Weilin Zhao, Ning Ding, Zhiyuan Liu, and Maosong Sun · 2021
Cited alongside, same era.
BERTese: Learning to speak to BERT
Adi Haviv, Jonathan Berant, and Amir Globerson · 2021
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
Reordering examples helps during priming-based few-shot learning
Sawan Kumar and Partha Talukdar · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
Cited alongside, same era.
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 · 2021
Cited alongside, same era.
Closest in time.
Principled instructions are all you need for questioning llama-1/2, gpt-3.5/4
Sondos Mahmoud Bsharat, Aidar Myrzakhan, and Zhiqiang Shen · 2023
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Symbolic discovery of optimization algorithms
Xiangning Chen, Chen Liang, Da Huang, Esteban Real, Kaiyuan Wang, Yao Liu, Hieu Pham, Xuanyi Dong, Thang Luong, Cho-Jui Hsieh, et al · 2023
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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
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Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality, mar. 2023, 2023
WL Chiang, Z Li, Z Lin, Y Sheng, Z Wu, H Zhang, L Zheng, S Zhuang, Y Zhuang, JE Gonzalez, et al · 2023
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Prompt expansion for adaptive text-to-image generation
Siddhartha Datta, Alexander Ku, Deepak Ramachandran, and Peter Anderson · 2023
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Qlora: Efficient finetuning of quantized llms
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer · 2023
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Active prompting with chain-of-thought for large language models, 2023
Shizhe Diao, Pengcheng Wang, Yong Lin, and Tong Zhang · 2023
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Raft: Reward ranked finetuning for generative foundation model alignment
Hanze Dong, Wei Xiong, Deepanshu Goyal, Rui Pan, Shizhe Diao, Jipeng Zhang, Kashun Shum, and Tong Zhang · 2023
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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 · 2023
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Pick-a-pic: An open dataset of user preferences for text-to-image generation
Yuval Kirstain, Adam Polyak, Uriel Singer, Shahbuland Matiana, Joe Penna, and Omer Levy · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Automatic prompt optimization with" gradient descent" and beam search
Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, and Michael Zeng · 2023
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Code llama: Open foundation models for code
Baptiste Roziere, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Tal Remez, Jérémy Rapin, et al · 2023
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Automatic prompt augmentation and selection with chain-of-thought from labeled data, 2023
KaShun Shum, Shizhe Diao, and Tong Zhang · 2023
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Gemini: a family of highly capable multimodal models
Gemini Team, Rohan Anil, Sebastian Borgeaud, Yonghui Wu, Jean-Baptiste Alayrac, Jiahui Yu, Radu Soricut, Johan Schalkwyk, Andrew M Dai, Anja Hauth, et al · 2023
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Gptfuzzer: Red teaming large language models with auto-generated jailbreak prompts
Jiahao Yu, Xingwei Lin, and Xinyu Xing · 2023
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Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Claire Cui, Olivier Bousquet, Quoc V Le, and Ed H. Chi · 2023
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Promptbench: A unified library for evaluation of large language models
Kaijie Zhu, Qinlin Zhao, Hao Chen, Jindong Wang, and Xing Xie · 2023
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Phi-2: The surprising power of small language models
Mojan Javaheripi and Sébastien Bubeck · 2024
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Pomp: Probability-driven meta-graph prompter for llms in low-resource unsupervised neural machine translation, 2024
Shilong Pan, Zhiliang Tian, Liang Ding, Zhen Huang, Zhihua Wen, and Dongsheng Li · 2024
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Tinyllama: An open-source small language model, 2024
Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, and Wei Lu · 2024
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