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Instruction-tuned Large Language Models (LLMs) show impressive results in numerous practical applications, but they lack essential safety features that are common in other areas of computer science, particularly an explicit separation of instructions and data.
Foundations of Cryptography
Oded Goldreich · 2001
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Data execution prevention
Hewlett Packard · 2005
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SQL injection attacks and defense
Justin Clarke-Salt · 2009
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The algorithmic foundations of differential privacy
Cynthia Dwork, Aaron Roth, et al · 2014
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SQuAD: 100,000+ questions for machine comprehension of text
Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang · 2016
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Computer Architecture: A Quantitative Approach
John L. Hennessy and David A. Patterson · 2017
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Handbook of Model Checking
Edmund M. Clarke, Thomas A. Henzinger, Helmut Veith, and Roderick Bloem · 2018
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared 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 M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher 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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AutoPrompt: Eliciting knowledge from language models with automatically generated prompts
Taylor Shin, Yasaman Razeghi, Robert L. Logan IV, Eric Wallace, and Sameer Singh · 2020
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TRL: Transformer reinforcement learning
Leandro von Werra, Younes Belkada, Lewis Tunstall, Edward Beeching, Tristan Thrush, Nathan Lambert, and Shengyi Huang · 2020
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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 P. Xing, and Zhiting Hu · 2022
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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 · 2022
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PEFT: State-of-the-art parameter-efficient fine-tuning methods
Sourab Mangrulkar, Sylvain Gugger, Lysandre Debut, Younes Belkada, Sayak Paul, and Benjamin Bossan · 2022
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Ignore previous prompt: Attack techniques for language models
Fábio Perez and Ian Ribeiro · 2022
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Fairness and machine learning: Limitations and opportunities
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2023
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Jailbreaking black box large language models in twenty queries
Patrick Chao, Alexander Robey, Edgar Dobriban, Hamed Hassani, George J. Pappas, and Eric Wong · 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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Not what you’ve signed up for: Compromising real-world LLM-integrated applications with indirect prompt injection
Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, Christoph Endres, Thorsten Holz, and Mario Fritz · 2023
Cited alongside, same era.
Lost in the middle: How language models use long contexts
Nelson F. Liu, Kevin Lin, John Hewitt, Ashwin Paranjape, Michele Bevilacqua, Fabio Petroni, and Percy Liang · 2023
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Introducing Microsoft 365 Copilot – your copilot for work
Microsoft · 2023
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Norman Mu, Sarah Chen, Zifan Wang, Sizhe Chen, David Karamardian, Lulwa Aljeraisy, Dan Hendrycks, and David Wagner · 2023
Cited alongside, same era.
Introducing GPTs
OpenAI · 2023
Cited alongside, same era.
Text generation models
StruQ: Defending against prompt injection with structured queries
Sizhe Chen, Julien Piet, Chawin Sitawarin, and David Wagner · 2024
Closest in time.
Gianluca De Stefano, Giancarlo Pellegrino, and Lea Schönherr · 2024
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AgentDojo: A dynamic environment to evaluate attacks and defenses for LLM agents
Edoardo Debenedetti, Jie Zhang, Mislav Balunović, Luca Beurer-Kellner, Marc Fischer, and Florian Tramèr · 2024
Closest in time.
Gemma: Open models based on Gemini research and technology
Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Léonard Hussenot, Pier Giuseppe Sessa, Aakanksha Chowdhery, Adam Roberts, Aditya Barua, Alex Botev, Alex Castro-Ros, Ambrose Slone, Amélie Héliou, Andrea Tacchetti, Anna Bulanova, Antonia Paterson, Beth Tsai, Bobak Shahriari, Charline Le Lan, Christopher A. Choquette-Choo, Clément Crepy, Daniel Cer, Daphne Ippolito, David Reid, Elena Buchatskaya, Eric Ni, Eric Noland, Geng Yan, George Tucker, George-Christian Muraru, Grigory Rozhdestvenskiy, Henryk Michalewski, Ian Tenney, Ivan Grishchenko, Jacob Austin, James Keeling, Jane Labanowski, Jean-Baptiste Lespiau, Jeff Stanway, Jenny Brennan, Jeremy Chen, Johan Ferret, Justin Chiu, Justin Mao-Jones, Katherine Lee, Kathy Yu, Katie Millican, Lars Lowe Sjoesund, Lisa Lee, Lucas Dixon, Machel Reid, Maciej Mikuła, Mateo Wirth, Michael Sharman, Nikolai Chinaev, Nithum Thain, Olivier Bachem, Oscar Chang, Oscar Wahltinez, Paige Bailey, Paul Michel, Petko Yotov, Rahma Chaabouni, Ramona Comanescu, Reena Jana, Rohan Anil, Ross McIlroy, Ruibo Liu, Ryan Mullins, Samuel L Smith, Sebastian Borgeaud, Sertan Girgin, Sholto Douglas, Shree Pandya, Siamak Shakeri, Soham De, Ted Klimenko, Tom Hennigan, Vlad Feinberg, Wojciech Stokowiec, Yu hui Chen, Zafarali Ahmed, Zhitao Gong, Tris Warkentin, Ludovic Peran, Minh Giang, Clément Farabet, Oriol Vinyals, Jeff Dean, Koray Kavukcuoglu, Demis Hassabis, Zoubin Ghahramani, Douglas Eck, Joelle Barral, Fernando Pereira, Eli Collins, Armand Joulin, Noah Fiedel, Evan Senter, Alek Andreev, and Kathleen Kenealy · 2024
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OpenAI · 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.
Alpaca: a strong, replicable instruction-following model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori Hashimoto · 2023
Cited alongside, same era.
Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, Dan Bikel, Lukas Blecher, Cristian Canton Ferrer, Moya Chen, Guillem Cucurull, David Esiobu, Jude Fernandes, Jeremy Fu, Wenyin Fu, Brian Fuller, Cynthia Gao, Vedanuj Goswami, Naman Goyal, Anthony Hartshorn, Saghar Hosseini, Rui Hou, Hakan Inan, Marcin Kardas, Viktor Kerkez, Madian Khabsa, Isabel Kloumann, Artem Korenev, Punit Singh Koura, Marie-Anne Lachaux, Thibaut Lavril, Jenya Lee, Diana Liskovich, Yinghai Lu, Yuning Mao, Xavier Martinet, Todor Mihaylov, Pushkar Mishra, Igor Molybog, Yixin Nie, Andrew Poulton, Jeremy Reizenstein, Rashi Rungta, Kalyan Saladi, Alan Schelten, Ruan Silva, Eric Michael Smith, Ranjan Subramanian, Xiaoqing Ellen Tan, Binh Tang, Ross Taylor, Adina Williams, Jian Xiang Kuan, Puxin Xu, Zheng Yan, Iliyan Zarov, Yuchen Zhang, Angela Fan, Melanie Kambadur, Sharan Narang, Aurelien Rodriguez, Robert Stojnic, Sergey Edunov, and Thomas Scialom · 2023
Cited alongside, same era.
Zephyr: Direct distillation of LM alignment
Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, Nathan Sarrazin, Omar Sanseviero, Alexander M. Rush, and Thomas Wolf · 2023
Cited alongside, same era.
Starling-7B: Improving LLM helpfulness & harmlessness with RLAIF
Banghua Zhu, Evan Frick, Tianhao Wu, Hanlin Zhu, Karthik Ganesan, Wei-Lin Chiang, Jian Zhang, and Jiantao Jiao · 2023
Cited alongside, same era.
Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Zico Kolter, and Matt Fredrikson · 2023
Cited alongside, same era.
Closest in time.
Defending against indirect prompt injection attacks with spotlighting
Keegan Hines, Gary Lopez, Matthew Hall, Federico Zarfati, Yonatan Zunger, and Emre Kiciman · 2024
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AutoDAN: Generating stealthy jailbreak prompts on aligned large language models
Xiaogeng Liu, Nan Xu, Muhao Chen, and Chaowei Xiao · 2024
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Using large language models for humanitarian frontline negotiation: Opportunities and considerations
Zilin Ma, Susannah, Su, Nathan Zhao, Linn Bieske, Blake Bullwinkel, Yanyi Zhang, Sophia, Yang, Ziqing Luo, Siyao Li, Gekai Liao, Boxiang Wang, Jinglun Gao, Zihan Wen, Claude Bruderlein, and Weiwei Pan · 2024
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Neural exec: Learning (and learning from) execution triggers for prompt injection attacks
Dario Pasquini, Martin Strohmeier, and Carmela Troncoso · 2024
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Jatmo: Prompt injection defense by task-specific finetuning
Julien Piet, Maha Alrashed, Chawin Sitawarin, Sizhe Chen, Zeming Wei, Elizabeth Sun, Basel Alomair, and David Wagner · 2024
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ConfusedPilot: Confused deputy risks in RAG-based LLMs
Ayush RoyChowdhury, Mulong Luo, Prateek Sahu, Sarbartha Banerjee, and Mohit Tiwari · 2024
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Harnessing LLMs for API interactions: A framework for classification and synthetic data generation
Chunliang Tao, Xiaojing Fan, and Yahe Yang · 2024
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The instruction hierarchy: Training LLMs to prioritize privileged instructions
Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke, and Alex Beutel · 2024
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Everything everywhere all at once: LLMs can in-context learn multiple tasks in superposition
Zheyang Xiong, Ziyang Cai, John Cooper, Albert Ge, Vasilis Papageorgiou, Zack Sifakis, Angeliki Giannou, Ziqian Lin, Liu Yang, Saurabh Agarwal, Grigorios G Chrysos, Samet Oymak, Kangwook Lee, and Dimitris Papailiopoulos · 2024
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Benchmarking and defending against indirect prompt injection attacks on large language models
Jingwei Yi, Yueqi Xie, Bin Zhu, Emre Kiciman, Guangzhong Sun, Xing Xie, and Fangzhao Wu · 2024
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Yi Zeng, Hongpeng Lin, Jingwen Zhang, Diyi Yang, Ruoxi Jia, and Weiyan Shi · 2024
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Robust prompt optimization for defending language models against jailbreaking attacks
Andy Zhou, Bo Li, and Haohan Wang · 2024
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