SafeSwitch: Steering Unsafe LLM Behavior via Internal Activation Signals

Thinking, fast and slow

Daniel Kahneman. 2011 · 2011

Earlier work this paper cites.

Squad: 100,000+ questions for machine comprehension of text

Original

P Rajpurkar. 2016 · 2016

Earlier work this paper cites.

Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension

Original

Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017 · 2017

Earlier work this paper cites.

Evaluating large language models trained on code

Original

Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde De Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, and 1 others. 2021 · 2021

Earlier work this paper cites.

Prefix-tuning: Optimizing continuous prompts for generation

Original

Xiang Lisa Li and Percy Liang. 2021 · 2021

Earlier work this paper cites.

Transformer feed-forward layers build predictions by promoting concepts in the vocabulary space

Original

Mor Geva, Avi Caciularu, Kevin Ro Wang, and Yoav Goldberg. 2022 · 2022

Earlier work this paper cites.

Locating and editing factual associations in gpt

Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. 2022 · 2022

Earlier work this paper cites.

Training language models to follow instructions with human feedback

Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, and 1 others. 2022 · 2022

Earlier work this paper cites.

Chain-of-thought prompting elicits reasoning in large language models

Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, and 1 others. 2022 · 2022

Earlier work this paper cites.

The internal state of an llm knows when it’s lying

Original

Amos Azaria and Tom Mitchell. 2023 · 2023

Earlier work this paper cites.

Eliciting latent predictions from transformers with the tuned lens

Original

Nora Belrose, Zach Furman, Logan Smith, Danny Halawi, Igor Ostrovsky, Lev McKinney, Stella Biderman, and Jacob Steinhardt. 2023 · 2023

Earlier work this paper cites.

Sparse autoencoders find highly interpretable features in language models

Original

Hoagy Cunningham, Aidan Ewart, Logan Riggs, Robert Huben, and Lee Sharkey. 2023 · 2023

Earlier work this paper cites.

Catastrophic jailbreak of open-source llms via exploiting generation

Original

Yangsibo Huang, Samyak Gupta, Mengzhou Xia, Kai Li, and Danqi Chen. 2023 · 2023

Earlier work this paper cites.

Mistral 7b

Original

Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, and 1 others. 2023 · 2023

Earlier work this paper cites.

Certifying llm safety against adversarial prompting

Original

Aounon Kumar, Chirag Agarwal, Suraj Srinivas, Aaron Jiaxun Li, Soheil Feizi, and Himabindu Lakkaraju. 2023 · 2023

Earlier work this paper cites.

Efficient memory management for large language model serving with pagedattention

Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, Ying Sheng, Lianmin Zheng, Cody Hao Yu, Joseph Gonzalez, Hao Zhang, and Ion Stoica. 2023 · 2023

Earlier work this paper cites.

Let’s verify step by step

Original

Hunter Lightman, Vineet Kosaraju, Yura Burda, Harri Edwards, Bowen Baker, Teddy Lee, Jan Leike, John Schulman, Ilya Sutskever, and Karl Cobbe. 2023 · 2023

Earlier work this paper cites.

Xstest: A test suite for identifying exaggerated safety behaviours in large language models

Original

Paul Röttger, Hannah Rose Kirk, Bertie Vidgen, Giuseppe Attanasio, Federico Bianchi, and Dirk Hovy. 2023 · 2023

Earlier work this paper cites.

On the planning abilities of large language models-a critical investigation

Karthik Valmeekam, Matthew Marquez, Sarath Sreedharan, and Subbarao Kambhampati. 2023 · 2023

Cited alongside, same era.

Do-not-answer: A dataset for evaluating safeguards in llms

Original

Yuxia Wang, Haonan Li, Xudong Han, Preslav Nakov, and Timothy Baldwin. 2023 · 2023

Cited alongside, same era.

Editing large language models: Problems, methods, and opportunities

Original

Yunzhi Yao, Peng Wang, Bozhong Tian, Siyuan Cheng, Zhoubo Li, Shumin Deng, Huajun Chen, and Ningyu Zhang. 2023 · 2023

Cited alongside, same era.

Unlearning bias in language models by partitioning gradients

Charles Yu, Sullam Jeoung, Anish Kasi, Pengfei Yu, and Heng Ji. 2023 · 2023

Cited alongside, same era.

Siren’s song in the ai ocean: a survey on hallucination in large language models

Original

Rule based rewards for language model safety

Original

Tong Mu, Alec Helyar, Johannes Heidecke, Joshua Achiam, Andrea Vallone, Ian Kivlichan, Molly Lin, Alex Beutel, John Schulman, and Lilian Weng. 2024 · 2024

Later among the works it cites.

Why does new knowledge create messy ripple effects in llms?

Jiaxin Qin, Zixuan Zhang, Chi Han, Manling Li, Pengfei Yu, and Heng Ji. 2024 · 2024

Later among the works it cites.

Representation noising: A defence mechanism against harmful finetuning

Domenic Rosati, Jan Wehner, Kai Williams, Lukasz Bartoszcze, Robie Gonzales, Subhabrata Majumdar, Hassan Sajjad, Frank Rudzicz, and 1 others. 2024 · 2024

Later among the works it cites.

A systematic survey of prompt engineering in large language models: Techniques and applications

Original

Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, and Aman Chadha. 2024 · 2024

Later among the works it cites.

” do anything now”: Characterizing and evaluating in-the-wild jailbreak prompts on large language models