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Actively inferring user preferences, for example by asking good questions, is important for any human-facing decision-making system.
Studies in the Way of Words
Paul Grice · 1991
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Active learning literature survey
Burr Settles · 2009
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Deep reinforcement learning from human preferences
Paul F Christiano, Jan Leike, Tom Brown, Miljan Martic, Shane Legg, and Dario Amodei · 2017
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Learning to ask good questions: Ranking clarification questions using neural expected value of perfect information
Sudha Rao and Hal Daumé III · 2018
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Asking clarifying questions in open-domain information-seeking conversations
Mohammad Aliannejadi, Hamed Zamani, Fabio Crestani, and W Bruce Croft · 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, et al · 2020
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Interactive classification by asking informative questions
Lili Yu, Howard Chen, Sida I. Wang, Tao Lei, and Yoav Artzi · 2020
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Generating clarifying questions for information retrieval
Hamed Zamani, Susan Dumais, Nick Craswell, Paul Bennett, and Gord Lueck · 2020
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David Dohan, Winnie Xu, Aitor Lewkowycz, Jacob Austin, David Bieber, Raphael Gontijo Lopes, Yuhuai Wu, Henryk Michalewski, Rif A Saurous, Jascha Sohl-Dickstein, et al · 2022
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Human-like few-shot learning via bayesian reasoning over natural language
Kevin Ellis · 2023
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Belinda Z Li, Alex Tamkin, Noah Goodman, and Jacob Andreas · 2023
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Gpt-4 technical report, 2023
OpenAI · 2023
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Llama: Open and efficient foundation language models
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Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Chain-of-thought prompting elicits reasoning in large language models
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