Fetching the paper…
Reading the bibliography…
RL-based techniques can be employed to search for prompts that, when fed into a target language model, maximize a set of user-specified reward functions.
Steepest descent methods for multicriteria optimization
Jörg Fliege and Benar Fux Svaiter. 2000 · 2000
Earlier work this paper cites.
Multiobjective optimization using evolutionary algorithms — a comparative case study
Eckart Zitzler and Lothar Thiele. 1998 · 2000
Earlier work this paper cites.
A probabilistic formulation of unsupervised text style transfer
Junxian He, Xinyi Wang, Graham Neubig, and Taylor Berg-Kirkpatrick. 2020 · 2002
Earlier work this paper cites.
Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei jing Zhu. 2002 · 2002
Earlier work this paper cites.
Bounded archiving using the lebesgue measure
Joshua Knowles, David Corne, and Mark Fleischer. 2004 · 2004
Earlier work this paper cites.
Paraphrasing for style
Wei Xu, Alan Ritter, Bill Dolan, Ralph Grishman, and Colin Cherry. 2012 · 2012
Earlier work this paper cites.
Scalarized multi-objective reinforcement learning: Novel design techniques
Kristof Van Moffaert, Madalina M Drugan, and Ann Nowé. 2013 · 2013
Earlier work this paper cites.
Multi-objective reinforcement learning using sets of pareto dominating policies
Kristof Van Moffaert and Ann Nowé. 2014 · 2014
Earlier work this paper cites.
Overview of the IWSLT 2017 evaluation campaign
Mauro Cettolo, Marcello Federico, Luisa Bentivogli, Jan Niehues, Sebastian Stüker, Katsuhito Sudoh, Koichiro Yoshino, and Christian Federmann. 2017 · 2017
Earlier work this paper cites.
Shakespearizing modern language using copy-enriched sequence to sequence models
Harsh Jhamtani, Varun Gangal, Eduard Hovy, and Eric Nyberg. 2017 · 2017
Earlier work this paper cites.
Pareto multi-task learning
Xi Lin, Hui-Ling Zhen, Zhenhua Li, Qing-Fu Zhang, and Sam Kwong. 2019 · 2019
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeff Wu, Rewon Child, David Luan, Dario Amodei, and Ilya Sutskever. 2019 · 2019
Earlier work this paper cites.
Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf. 2019 · 2019
Earlier work this paper cites.
Multi-task learning as multi-objective optimization
Ozan Sener and Vladlen Koltun. 2019 · 2019
Cited alongside, same era.
A generalized algorithm for multi-objective reinforcement learning and policy adaptation
Runzhe Yang, Xingyuan Sun, and Karthik Narasimhan. 2019 · 2019
Cited alongside, same era.
How can we know what language models know?
Zhengbao Jiang, Frank F. Xu, Jun Araki, and Graham Neubig. 2020 · 2020
Cited alongside, same era.
Exploiting cloze-questions for few-shot text classification and natural language inference
Timo Schick and Hinrich Schütze. 2020 · 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 · 2020
Cited alongside, same era.
A practical guide to multi-objective reinforcement learning and planning
Conor F Hayes, Roxana Rădulescu, Eugenio Bargiacchi, Johan Källström, Matthew Macfarlane, Mathieu Reymond, Timothy Verstraeten, Luisa M Zintgraf, Richard Dazeley, Fredrik Heintz, et al. 2022 · 2022
Later among the works it cites.
Deep learning for text style transfer: A survey
Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, and Rada Mihalcea. 2022 · 2022
Later among the works it cites.
Reframing instructional prompts to GPTk’s language
Swaroop Mishra, Daniel Khashabi, Chitta Baral, Yejin Choi, and Hannaneh Hajishirzi. 2022 · 2022
Later among the works it cites.
SPoT: Better frozen model adaptation through soft prompt transfer
Tu Vu, Brian Lester, Noah Constant, Rami Al-Rfou’, and Daniel Cer. 2022 · 2022
Later among the works it cites.
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 · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Prediction-guided multi-objective reinforcement learning for continuous robot control
Jie Xu, Yunsheng Tian, Pingchuan Ma, Daniela Rus, Shinjiro Sueda, and Wojciech Matusik. 2020 · 2020
Cited alongside, same era.
Bertscore: Evaluating text generation with bert
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang. 2021 · 2021
Cited alongside, same era.
Learning how to ask: Querying LMs with mixtures of soft prompts
Guanghui Qin and Jason Eisner. 2021 · 2021
Cited alongside, same era.
Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2022 · 2022
Cited alongside, same era.
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 · 2022
Cited alongside, same era.
Efficient (soft) q-learning for text generation with limited good data
Han Guo, Bowen Tan, Zhengzhong Liu, Eric P. Xing, and Zhiting Hu. 2022 · 2022
Cited alongside, same era.
Personalized soups: Personalized large language model alignment via post-hoc parameter merging
Joel Jang, Seungone Kim, Bill Yuchen Lin, Yizhong Wang, Jack Hessel, Luke Zettlemoyer, Hannaneh Hajishirzi, Yejin Choi, and Prithviraj Ammanabrolu. 2023 · 2023
Later among the works it cites.
Discrete prompt compression with reinforcement learning
Hoyoun Jung and Kyung-Joong Kim. 2023 · 2023
Later among the works it cites.
Gpt understands, too
Xiao Liu, Yanan Zheng, Zhengxiao Du, Ming Ding, Yujie Qian, Zhilin Yang, and Jie Tang. 2023 · 2023
Later among the works it cites.
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.
Promptagent: Strategic planning with language models enables expert-level prompt optimization
Xinyuan Wang, Chenxi Li, Zhen Wang, Fan Bai, Haotian Luo, Jiayou Zhang, Nebojsa Jojic, Eric P Xing, and Zhiting Hu. 2023 · 2023
Later among the works it cites.
Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery
Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, and Tom Goldstein. 2023 · 2023
Later among the works it cites.
TEMPERA: Test-time prompt editing via reinforcement learning
Tianjun Zhang, Xuezhi Wang, Denny Zhou, Dale Schuurmans, and Joseph E. Gonzalez. 2023 · 2023
Later among the works it cites.