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Going beyond mimicking limited human experiences, recent studies show initial evidence that, like humans, large language models (LLMs) are capable of improving their abilities purely by self-correction, i.e., correcting previous responses through self-examination, in certain circumstances.
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Constitutional ai: Harmlessness from ai feedback
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What can transformers learn in-context? a case study of simple function classes
Shivam Garg, Dimitris Tsipras, Percy S Liang, and Gregory Valiant · 2022
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Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke E. Miller, Maddie Simens, Amanda Askell, P. Welinder, P. Christiano, J. Leike, and Ryan J. Lowe · 2022
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Chaos is a ladder: A new theoretical understanding of contrastive learning via augmentation overlap
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A closer look at in-context learning under distribution shifts
Kartik Ahuja and David Lopez-Paz · 2023
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Detecting language model attacks with perplexity
Gabriel Alon and Michael Kamfonas · 2023
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Transformers as statisticians: Provable in-context learning with in-context algorithm selection
Yu Bai, Fan Chen, Huan Wang, Caiming Xiong, and Song Mei · 2023
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Understanding in-context learning in transformers and llms by learning to learn discrete functions
Satwik Bhattamishra, Arkil Patel, Phil Blunsom, and Varun Kanade · 2023
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Defending against alignment-breaking attacks via robustly aligned llm
Bochuan Cao, Yuanpu Cao, Lu Lin, and Jinghui Chen · 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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Teaching large language models to self-debug
Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou · 2023
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Transformers implement functional gradient descent to learn non-linear functions in context
Xiang Cheng, Yuxin Chen, and Suvrit Sra · 2023
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Contrastive chain-of-thought prompting
Yew Ken Chia, Guizhen Chen, Luu Anh Tuan, Soujanya Poria, and Lidong Bing · 2023
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Multilingual jailbreak challenges in large language models
Yue Deng, Wenxuan Zhang, Sinno Jialin Pan, and Lidong Bing · 2023
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Causallm is not optimal for in-context learning
Nan Ding, Tomer Levinboim, Jialin Wu, Sebastian Goodman, and Radu Soricut · 2023
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Transformers learn higher-order optimization methods for in-context learning: A study with linear models
Deqing Fu, Tian-Qi Chen, Robin Jia, and Vatsal Sharan · 2023
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Can large language models really improve by self-critiquing their own plans?
Karthik Valmeekam, Matthew Marquez, and Subbarao Kambhampati · 2023
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The art of defending: A systematic evaluation and analysis of llm defense strategies on safety and over-defensiveness
Neeraj Varshney, Pavel Dolin, Agastya Seth, and Chitta Baral · 2023
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”vicuna: An open-source chatbot impressing gpt-4 with 90quality, 2023
Vicuna · 2023
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Transformers learn in-context by gradient descent
Johannes Von Oswald, Eyvind Niklasson, Ettore Randazzo, João Sacramento, Alexander Mordvintsev, Andrey Zhmoginov, and Max Vladymyrov · 2023
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Jailbreak and guard aligned language models with only few in-context demonstrations
Zeming Wei, Yifei Wang, and Yisen Wang · 2023
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Deep Ganguli, Amanda Askell, Nicholas Schiefer, Thomas I. Liao, Kamilė Lukošiūtė, Anna Chen, Anna Goldie, Azalia Mirhoseini, Catherine Olsson, Danny Hernandez, Dawn Drain, Dustin Li, Eli Tran-Johnson, Ethan Perez, Jackson Kernion, Jamie Kerr, Jared Mueller, Joshua Landau, Kamal Ndousse, Karina Nguyen, Liane Lovitt, Michael Sellitto, Nelson Elhage, Noemi Mercado, Nova DasSarma, Oliver Rausch, Robert Lasenby, Robin Larson, Sam Ringer, Sandipan Kundu, Saurav Kadavath, Scott Johnston, Shauna Kravec, Sheer El Showk, Tamera Lanham, Timothy Telleen-Lawton, Tom Henighan, Tristan Hume, Yuntao Bai, Zac Hatfield-Dodds, Ben Mann, Dario Amodei, Nicholas Joseph, Sam McCandlish, Tom Brown, Christopher Olah, Jack Clark, Samuel R. Bowman, and Jared Kaplan · 2023
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In-context alignment: Chat with vanilla language models before fine-tuning
Xiaochuang Han · 2023
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Baseline defenses for adversarial attacks against aligned language models
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Ziwei Ji, Tiezheng Yu, Yan Xu, Nayeon Lee, Etsuko Ishii, and Pascale Fung · 2023
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Mistral 7b
Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, and William El Sayed · 2023
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Language models can solve computer tasks
Geunwoo Kim, Pierre Baldi, and Stephen McAleer · 2023
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Certifying llm safety against adversarial prompting
Aounon Kumar, Chirag Agarwal, Suraj Srinivas, Soheil Feizi, and Hima Lakkaraju · 2023
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How many pretraining tasks are needed for in-context learning of linear regression?
Jingfeng Wu, Difan Zou, Zixiang Chen, Vladimir Braverman, Quanquan Gu, and Peter L. Bartlett · 2023
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Defending chatgpt against jailbreak attack via self-reminders
Yueqi Xie, Jingwei Yi, Jiawei Shao, Justin Curl, Lingjuan Lyu, Qifeng Chen, Xing Xie, and Fangzhao Wu · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
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Low-resource languages jailbreak gpt-4
Zheng-Xin Yong, Cristina Menghini, and Stephen H. Bach · 2023
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Judging llm-as-a-judge with mt-bench and chatbot arena
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng, Siyuan Zhuang, Zhanghao Wu, Yonghao Zhuang, Zi Lin, Zhuohan Li, Dacheng Li, Eric P. Xing, Hao Zhang, Joseph E. Gonzalez, and Ion Stoica · 2023
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Autodan: Automatic and interpretable adversarial attacks on large language models
Sicheng Zhu, Ruiyi Zhang, Bang An, Gang Wu, Joe Barrow, Zichao Wang, Furong Huang, Ani Nenkova, and Tong Sun · 2023
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Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, J. Zico Kolter, and Matt Fredrikson · 2023
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Foundational challenges in assuring alignment and safety of large language models
Usman Anwar, Abulhair Saparov, Javier Rando, Daniel Paleka, Miles Turpin, Peter Hase, Ekdeep Singh Lubana, Erik Jenner, Stephen Casper, Oliver Sourbut, et al · 2024
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When is tree search useful for llm planning? it depends on the discriminator
Ziru Chen, Michael White, Raymond Mooney, Ali Payani, Yu Su, and Huan Sun · 2024
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Zhichen Dong, Zhanhui Zhou, Chao Yang, Jing Shao, and Yu Qiao · 2024
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Llms can find mathematical reasoning mistakes by pedagogical chain-of-thought
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Criticbench: Benchmarking llms for critique-correct reasoning
Zicheng Lin, Zhibin Gou, Tian Liang, Ruilin Luo, Haowei Liu, and Yujiu Yang · 2024
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Fight back against jailbreaking via prompt adversarial tuning
Yichuan Mo, Yuji Wang, Zeming Wei, and Yisen Wang · 2024
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OpenAI · 2024
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GPT-4 is too smart to be safe: Stealthy chat with LLMs via cipher
Youliang Yuan, Wenxiang Jiao, Wenxuan Wang, Jen tse Huang, Pinjia He, Shuming Shi, and Zhaopeng Tu · 2024
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Small language models need strong verifiers to self-correct reasoning
Yunxiang Zhang, Muhammad Khalifa, Lajanugen Logeswaran, Jaekyeom Kim, Moontae Lee, Honglak Lee, and Lu Wang · 2024
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