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Language models are capable of iteratively improving their outputs based on natural language feedback, thus enabling in-context optimization of user preference.
Concrete problems in AI safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul F. Christiano, John Schulman, and Dan Mané. 2016 · 2016
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Language models are few-shot learners. arxiv 2020
TB Brown, B Mann, N Ryder, M Subbiah, J Kaplan, P Dhariwal, A Neelakantan, P Shyam, G Sastry, A Askell, et al · 2020
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markov-essays
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Learning to summarize from human feedback
Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul Christiano. 2020 · 2020
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A general language assistant as a laboratory for alignment
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Constitutional ai: Harmlessness from ai feedback
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Self-critiquing models for assisting human evaluators
William Saunders, Catherine Yeh, Jeff Wu, Steven Bills, Long Ouyang, Jonathan Ward, and Jan Leike. 2022 · 2022
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Defining and characterizing reward gaming
Joar Skalse, Nikolaus Howe, Dmitrii Krasheninnikov, and David Krueger. 2022 · 2022
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Visit-bench: A benchmark for vision-language instruction following inspired by real-world use
Yonatan Bitton, Hritik Bansal, Jack Hessel, Rulin Shao, Wanrong Zhu, Anas Awadalla, Josh Gardner, Rohan Taori, and Ludwig Schimdt. 2023 · 2023
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Teaching large language models to self-debug
Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou. 2023 · 2023
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Can large language models be an alternative to human evaluations?
Cheng-Han Chiang and Hung-yi Lee. 2023 · 2023
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Ai: new source of competitiveness in higher education
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Prd: Peer rank and discussion improve large language model based evaluations
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Using artificial intelligence to assess personal qualities in college admissions
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Can large language models really improve by self-critiquing their own plans?
Karthik Valmeekam, Matthew Marquez, and Subbarao Kambhampati. 2023 · 2023
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Aojun Zhou, Ke Wang, Zimu Lu, Weikang Shi, Sichun Luo, Zipeng Qin, Shaoqing Lu, Anya Jia, Linqi Song, Mingjie Zhan, and Hongsheng Li. 2023 · 2023
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Benchmarking foundation models with language-model-as-an-examiner
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Critic: Large language models can self-correct with tool-interactive critiquing
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Critique ability of large language models
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Self-refine: Iterative refinement with self-feedback
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Feedback loops with language models drive in-context reward hacking
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Perils of self-feedback: Self-bias amplifies in large language models
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Judging llm-as-a-judge with mt-bench and chatbot arena
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