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System prompts that include detailed instructions to describe the task performed by the underlying LLM can easily transform foundation models into tools and services with minimal overhead.
Policy shaping: Integrating human feedback with reinforcement learning
Shane Griffith, Kaushik Subramanian, Jonathan Scholz, Charles L Isbell, and Andrea L Thomaz · 2013
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Abstractive text summarization using sequence-to-sequence rnns and beyond, 2016
Ramesh Nallapati, Bowen Zhou, Cicero Nogueira dos santos, Caglar Gulcehre, and Bing Xiang · 2016
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension, 2017
Mandar Joshi, Eunsol Choi, Daniel S. Weld, and Luke Zettlemoyer · 2017
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Adam: A method for stochastic optimization, 2017
Diederik P. Kingma and Jimmy Ba · 2017
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Bert: Pre-training of deep bidirectional transformers for language understanding, 2019
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Samsum corpus: A human-annotated dialogue dataset for abstractive summarization
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer · 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, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Lora: Low-rank adaptation of large language models, 2021
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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The power of scale for parameter-efficient prompt tuning, 2021
Brian Lester, Rami Al-Rfou, and Noah Constant · 2021
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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
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Prompt waywardness: The curious case of discretized interpretation of continuous prompts
Daniel Khashabi, Xinxi Lyu, Sewon Min, Lianhui Qin, Kyle Richardson, Sean Welleck, Hannaneh Hajishirzi, Tushar Khot, Ashish Sabharwal, Sameer Singh, et al · 2022
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Truthfulqa: Measuring how models mimic human falsehoods, 2022
Stephanie Lin, Jacob Hilton, and Owain Evans · 2022
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Tempera: Test-time prompt editing via reinforcement learning
Tianjun Zhang, Xuezhi Wang, Denny Zhou, Dale Schuurmans, and Joseph E Gonzalez · 2022
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Introducing GPTs, 2023
2023
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Detecting language model attacks with perplexity, 2023
Gabriel Alon and Michael Kamfonas · 2023
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Using GPT-eliezer against ChatGPT jailbreaking, 2023
Stuart Armstrong and R Gorman · 2023
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Abusing images and sounds for indirect instruction injection in multi-modal llms, 2023
Eugene Bagdasaryan, Tsung-Yin Hsieh, Ben Nassi, and Vitaly Shmatikov · 2023
Cited alongside, same era.
Extracting training data from diffusion models
Nicolas Carlini, Jamie Hayes, Milad Nasr, Matthew Jagielski, Vikash Sehwag, Florian Tramer, Borja Balle, Daphne Ippolito, and Eric Wallace · 2023
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Can language models be instructed to protect personal information?, 2023
Yang Chen, Ethan Mendes, Sauvik Das, Wei Xu, and Alan Ritter · 2023
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Machine-generated text: A comprehensive survey of threat models and detection methods
Evan Crothers, Nathalie Japkowicz, and Herna L Viktor · 2023
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QLoRA: Efficient finetuning of quantized LLMs
Tim Dettmers, Artidoro Pagnoni, Ari Holtzman, and Luke Zettlemoyer · 2023
Cited alongside, same era.
Propile: Probing privacy leakage in large language models
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 Qizhe Xie, and Junxian He · 2024
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Whispers in the machine: Confidentiality in llm-integrated systems
Jonathan Evertz, Merlin Chlosta, Lea Schönherr, and Thorsten Eisenhofer · 2024
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Leaked-gpts
friuns2 · 2024
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Does fine-tuning llms on new knowledge encourage hallucinations?, 2024
Zorik Gekhman, Gal Yona, Roee Aharoni, Matan Eyal, Amir Feder, Roi Reichart, and Jonathan Herzig · 2024
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Openassistant conversations-democratizing large language model alignment
Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, Sotiris Anagnostidis, Zhi Rui Tam, Keith Stevens, Abdullah Barhoum, Duc Nguyen, Oliver Stanley, Richárd Nagyfi, et al · 2024
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Dialogue for prompting: A policy-gradient-based discrete prompt generation for few-shot learning
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Siwon Kim, Sangdoo Yun, Hwaran Lee, Martin Gubri, Sungroh Yoon, and Seong Joon Oh · 2023
Cited alongside, same era.
Prompt injection attacks and defenses in llm-integrated applications, 2023
Yupei Liu, Yuqi Jia, Runpeng Geng, Jinyuan Jia, and Neil Zhenqiang Gong · 2023
Cited alongside, same era.
Annual income of prompt engineers in the us (chatgpt), nov 2023
Christian Mayer · 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
Cited alongside, same era.
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 Xing, and Zhiting Hu · 2023
Cited alongside, same era.
Aligning large language models with human: A survey, 2023
Yufei Wang, Wanjun Zhong, Liangyou Li, Fei Mi, Xingshan Zeng, Wenyong Huang, Lifeng Shang, Xin Jiang, and Qun Liu · 2023
Cited alongside, same era.
Hard prompts made easy: Gradient-based discrete optimization for prompt tuning and discovery, 2023
Yuxin Wen, Neel Jain, John Kirchenbauer, Micah Goldblum, Jonas Geiping, and Tom Goldstein · 2023
Cited alongside, same era.
Chengzhengxu Li, Xiaoming Liu, Yichen Wang, Duyi Li, Yu Lan, and Chao Shen · 2024
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Prompt injection attack against llm-integrated applications, 2024
Yi Liu, Gelei Deng, Yuekang Li, Kailong Wang, Zihao Wang, Xiaofeng Wang, Tianwei Zhang, Yepang Liu, Haoyu Wang, Yan Zheng, and Yang Liu · 2024
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Jailbreaking chatgpt via prompt engineering: An empirical study, 2024
Yi Liu, Gelei Deng, Zhengzi Xu, Yuekang Li, Yaowen Zheng, Ying Zhang, Lida Zhao, Tianwei Zhang, Kailong Wang, and Yang Liu · 2024
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Generative ai in eu law: Liability, privacy, intellectual property, and cybersecurity, 2024
Claudio Novelli, Federico Casolari, Philipp Hacker, Giorgio Spedicato, and Luciano Floridi · 2024
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Gpt-4 technical report, 2024
OpenAI · 2024
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Fine-tuning aligned language models compromises safety, even when users do not intend to!
Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson · 2024
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Code llama: Open foundation models for code, 2024
Baptiste Rozière, Jonas Gehring, Fabian Gloeckle, Sten Sootla, Itai Gat, Xiaoqing Ellen Tan, Yossi Adi, Jingyu Liu, Romain Sauvestre, Tal Remez, Jérémy Rapin, Artyom Kozhevnikov, Ivan Evtimov, Joanna Bitton, Manish Bhatt, Cristian Canton Ferrer, Aaron Grattafiori, Wenhan Xiong, Alexandre Défossez, Jade Copet, Faisal Azhar, Hugo Touvron, Louis Martin, Nicolas Usunier, Thomas Scialom, and Gabriel Synnaeve · 2024
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Raccoon: Prompt extraction benchmark of llm-integrated applications
Junlin Wang, Tianyi Yang, Roy Xie, and Bhuwan Dhingra · 2024
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Effective prompt extraction from language models, 2024
Yiming Zhang, Nicholas Carlini, and Daphne Ippolito · 2024
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Proxyprompt: Securing system prompts against prompt extraction attacks, 2025
Zhixiong Zhuang, Maria-Irina Nicolae, Hui-Po Wang, and Mario Fritz · 2025
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