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Reward models (RMs) are essential for aligning large language models (LLMs) with human preferences to improve interaction quality.
The technique of clear writing
Robert Gunning · 1952
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A statistical interpretation of term specificity and its application in retrieval
Karen Sparck Jones · 1972
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A computer readability formula designed for machine scoring
Meri Coleman · 1975
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Derivation of new readability formulas (automated readability index, fog count and flesch reading ease formula) for navy enlisted personnel
J Peter Kincaid, Robert P Fishburne Jr, Richard L Rogers, and Brad S Chissom · 1975
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The influence of the sigmoid function parameters on the speed of backpropagation learning
Jun Han and Claudio Moraga · 1995
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API design for machine learning software: experiences from the scikit-learn project
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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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Can neural machine translation be improved with user feedback?
Julia Kreutzer, Shahram Khadivi, Evgeny Matusov, and Stefan Riezler · 2018
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Scibert: A pretrained language model for scientific text
Iz Beltagy, Kyle Lo, and Arman Cohan · 2019
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Better rewards yield better summaries: Learning to summarise without references
Florian Böhm, Yang Gao, Christian M Meyer, Ori Shapira, Ido Dagan, and Iryna Gurevych · 2019
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Fine-tuning language models from human preferences
Daniel M Ziegler, Nisan Stiennon, Jeffrey Wu, Tom B Brown, Alec Radford, Dario Amodei, Paul Christiano, and Geoffrey Irving · 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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Learning to summarize with human feedback
Nisan Stiennon, Long Ouyang, Jeffrey Wu, Daniel Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul F Christiano · 2020
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A general language assistant as a laboratory for alignment
Amanda Askell, Yuntao Bai, Anna Chen, Dawn Drain, Deep Ganguli, Tom Henighan, Andy Jones, Nicholas Joseph, Ben Mann, Nova DasSarma, et al · 2021
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Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon · 2021
Bridging the gap: A survey on integrating (human) feedback for natural language generation
Patrick Fernandes, Aman Madaan, Emmy Liu, António Farinhas, Pedro Henrique Martins, Amanda Bertsch, José GC de Souza, Shuyan Zhou, Tongshuang Wu, Graham Neubig, et al · 2023
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Llava-med: Training a large language-and-vision assistant for biomedicine in one day
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Webgpt: Browser-assisted question-answering with human feedback
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Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned
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Emergent abilities of large language models
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Training a helpful and harmless assistant with reinforcement learning from human feedback
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Constitutional ai: Harmlessness from ai feedback
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Llama 2: Open foundation and fine-tuned chat models
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Rrhf: Rank responses to align language models with human feedback without tears
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