Fetching the paper…
Reading the bibliography…
Large language models (LLMs) have transformed AI across diverse domains, with prompting being central to their success in guiding model outputs.
Benchmarking optimization software with performance profiles
Elizabeth D Dolan and Jorge J Moré · 2002
Earlier work this paper cites.
Program induction by rationale generation: Learning to solve and explain algebraic word problems
Wang Ling, Dani Yogatama, Chris Dyer, and Phil Blunsom · 2017
Earlier work this paper cites.
Training verifiers to solve math word problems, 2021
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
Earlier work this paper cites.
Evaluating the robustness of neural language models to input perturbations
Milad Moradi and Matthias Samwald · 2021
Earlier work this paper cites.
Are nlp models really able to solve simple math word problems?
Arkil Patel, Satwik Bhattamishra, and Navin Goyal · 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 Xing, and Zhiting Hu · 2022
Earlier work this paper cites.
What makes good in-context examples for GPT-3?
Jiachang Liu, Dinghan Shen, Yizhe Zhang, Bill Dolan, Lawrence Carin, and Weizhu Chen · 2022
Earlier work this paper cites.
Text and patterns: For effective chain of thought, it takes two to tango, 2022
Aman Madaan and Amir Yazdanbakhsh · 2022
Earlier work this paper cites.
Learning to retrieve prompts for in-context learning, 2022
Ohad Rubin, Jonathan Herzig, and Jonathan Berant · 2022
Earlier work this paper cites.
Challenging big-bench tasks and whether chain-of-thought can solve them, 2022
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V. Le, Ed H. Chi, Denny Zhou, and Jason Wei · 2022
Earlier work this paper cites.
Finetuned language models are zero-shot learners, 2022
Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le · 2022
Earlier work this paper cites.
Active example selection for in-context learning
Yiming Zhang, Shi Feng, and Chenhao Tan · 2022
Cited alongside, same era.
Instructzero: Efficient instruction optimization for black-box large language models, 2023
Lichang Chen, Jiuhai Chen, Tom Goldstein, Heng Huang, and Tianyi Zhou · 2023
Cited alongside, same era.
Promptbreeder: Self-referential self-improvement via prompt evolution, 2023
Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, and Tim Rocktäschel · 2023
Cited alongside, same era.
Gpt understands, too, 2023
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang · 2023
Cited alongside, same era.
How well do sota legal reasoning models support abductive reasoning?, 2023
Ha-Thanh Nguyen, Randy Goebel, Francesca Toni, Kostas Stathis, and Ken Satoh · 2023
Cited alongside, same era.
Complementary explanations for effective in-context learning, 2023
Xi Ye, Srinivasan Iyer, Asli Celikyilmaz, Ves Stoyanov, Greg Durrett, and Ramakanth Pasunuru · 2023
Later among the works it cites.
Large language models are human-level prompt engineers, 2023
Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, Keiran Paster, Silviu Pitis, Harris Chan, and Jimmy Ba · 2023
Later among the works it cites.
On the relation between sensitivity and accuracy in in-context learning, 2024
Yanda Chen, Chen Zhao, Zhou Yu, Kathleen McKeown, and He He · 2024
Closest in time.
Saullm-7b: A pioneering large language model for law, 2024
Pierre Colombo, Telmo Pessoa Pires, Malik Boudiaf, Dominic Culver, Rui Melo, Caio Corro, Andre F. T. Martins, Fabrizio Esposito, Vera Lúcia Raposo, Sofia Morgado, and Michael Desa · 2024
Closest in time.
Prompt optimization via adversarial in-context learning, 2024
Xuan Long Do, Yiran Zhao, Hannah Brown, Yuxi Xie, James Xu Zhao, Nancy F. Chen, Kenji Kawaguchi, Michael Shieh, and Junxian He · 2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Can generalist foundation models outcompete special-purpose tuning? case study in medicine, 2023
Harsha Nori, Yin Tat Lee, Sheng Zhang, Dean Carignan, Richard Edgar, Nicolo Fusi, Nicholas King, Jonathan Larson, Yuanzhi Li, Weishung Liu, Renqian Luo, Scott Mayer McKinney, Robert Osazuwa Ness, Hoifung Poon, Tao Qin, Naoto Usuyama, Chris White, and Eric Horvitz · 2023
Cited alongside, same era.
Automatic prompt optimization with "gradient descent" and beam search, 2023
Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, and Michael Zeng · 2023
Cited alongside, same era.
Autohint: Automatic prompt optimization with hint generation, 2023
Hong Sun, Xue Li, Yinchuan Xu, Youkow Homma, Qi Cao, Min Wu, Jian Jiao, and Denis Charles · 2023
Cited alongside, same era.
Llama: Open and efficient foundation language models, 2023
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, and Guillaume Lample · 2023
Cited alongside, same era.
Chain-of-thought prompting elicits reasoning in large language models, 2023
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou · 2023
Cited alongside, same era.
Expertprompting: Instructing large language models to be distinguished experts, 2023
Benfeng Xu, An Yang, Junyang Lin, Quan Wang, Chang Zhou, Yongdong Zhang, and Zhendong Mao · 2023
Cited alongside, same era.
Towards understanding chain-of-thought prompting: An empirical study of what matters, 2023a
Boshi Wang, Sewon Min, Xiang Deng, Jiaming Shen, You Wu, Luke Zettlemoyer, and Huan Sun
Cited in the paper.
Connecting large language models with evolutionary algorithms yields powerful prompt optimizers, 2024
Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, and Yujiu Yang · 2024
Closest in time.
Xiaoqiang Lin, Zhaoxuan Wu, Zhongxiang Dai, Wenyang Hu, Yao Shu, See-Kiong Ng, Patrick Jaillet, and Bryan Kian Hsiang Low · 2024
Closest in time.
Gpt-4 technical report, 2024
OpenAI, R, and other et. al · 2024
Closest in time.
Teach better or show smarter? on instructions and exemplars in automatic prompt optimization, 2024
Xingchen Wan, Ruoxi Sun, Hootan Nakhost, and Sercan O. Arik · 2024
Closest in time.
Large language models as optimizers, 2024
Chengrun Yang, Xuezhi Wang, Yifeng Lu, Hanxiao Liu, Quoc V. Le, Denny Zhou, and Xinyun Chen · 2024
Closest in time.
Alpacare:instruction-tuned large language models for medical application, 2024
Xinlu Zhang, Chenxin Tian, Xianjun Yang, Lichang Chen, Zekun Li, and Linda Ruth Petzold · 2024
Closest in time.