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Aligning large language models (LLMs) with human preferences has been recognized as the key to improving LLMs' interaction quality.
Sticking to the facts: Confident decoding for faithful data-to-text generation
Ran Tian, Shashi Narayan, Thibault Sellam, and Ankur P Parikh. 2019 · 1910
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The method of paired comparisons , volume 12
Herbert Aron David. 1963 · 1963
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The comparison and evaluation of forecasters
Morris H DeGroot and Stephen E Fienberg. 1983 · 1983
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Residual energy-based models for text generation
Yuntian Deng, Anton Bakhtin, Myle Ott, Arthur Szlam, and Marc’Aurelio Ranzato. 2020 · 2004
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Learning all optimal policies with multiple criteria
Leon Barrett and Srini Narayanan. 2008 · 2008
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Toward seamless prediction: Calibration of climate change projections using seasonal forecasts
TN Palmer, FJ Doblas-Reyes, Antje Weisheimer, and MJ Rodwell. 2008 · 2008
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Multi-task learning with deep neural networks: A survey
Michael Crawshaw. 2020 · 2009
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A survey on transfer learning
Sinno Jialin Pan and Qiang Yang. 2009 · 2009
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Human-centric dialog training via offline reinforcement learning
Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Shane Gu, and Rosalind Picard. 2020 · 2010
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Nurses’ risk assessment judgements: A confidence calibration study
Huiqin Yang and Carl Thompson. 2010 · 2010
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Revisiting frank-wolfe: Projection-free sparse convex optimization
Martin Jaggi. 2013 · 2013
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht. 2015 · 2015
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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 · 2017
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A comprehensive survey on domain adaptation for visual applications
Gabriela Csurka. 2017 · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger. 2017 · 2017
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2017 · 2017
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Communication-efficient learning of deep networks from decentralized data
Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017 · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov. 2017 · 2017
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A review of multi-objective optimization: Methods and its applications
Nyoman Gunantara. 2018 · 2018
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Not all samples are created equal: Deep learning with importance sampling
Angelos Katharopoulos and François Fleuret. 2018 · 2018
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Scalable agent alignment via reward modeling: a research direction
Jan Leike, David Krueger, Tom Everitt, Miljan Martic, Vishal Maini, and Shane Legg. 2018 · 2018
Cited alongside, same era.
Multi-task learning as multi-objective optimization
Ozan Sener and Vladlen Koltun. 2018 · 2018
Cited alongside, same era.
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
Cited alongside, same era.
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 · 2021
Cited alongside, same era.
Agreeing to disagree: Annotating offensive language datasets with annotators’ disagreement
Elisa Leonardelli, Stefano Menini, Alessio Palmero Aprosio, Marco Guerini, and Sara Tonelli. 2021 · 2021
Personalized soups: Personalized large language model alignment via post-hoc parameter merging
Joel Jang, Seungone Kim, Bill Yuchen Lin, Yizhong Wang, Jack Hessel, Luke Zettlemoyer, Hannaneh Hajishirzi, Yejin Choi, and Prithviraj Ammanabrolu. 2023 · 2023
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Openassistant conversations - democratizing large language model alignment
Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, Sotiris Anagnostidis, Zhi-Rui Tam, Keith Stevens, Abdullah Barhoum, Nguyen Minh Duc, Oliver Stanley, Richárd Nagyfi, Shahul ES, Sameer Suri, David Glushkov, Arnav Dantuluri, Andrew Maguire, Christoph Schuhmann, Huu Nguyen, and Alexander Mattick. 2023 · 2023
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Statistical rejection sampling improves preference optimization
Tianqi Liu, Yao Zhao, Rishabh Joshi, Misha Khalman, Mohammad Saleh, Peter J Liu, and Jialu Liu. 2023 · 2023
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ChatGPT, Mar 14 version
OpenAI. 2023 · 2023
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Cited alongside, same era.
Learning to diversify for single domain generalization
Zijian Wang, Yadan Luo, Ruihong Qiu, Zi Huang, and Mahsa Baktashmotlagh. 2021 · 2021
Cited alongside, same era.
On hallucination and predictive uncertainty in conditional language generation
Yijun Xiao and William Yang Wang. 2021 · 2021
Cited alongside, same era.
Training a helpful and harmless assistant with reinforcement learning from human feedback
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. 2022 · 2022
Cited alongside, same era.
Dealing with disagreements: Looking beyond the majority vote in subjective annotations
Aida Mostafazadeh Davani, Mark Díaz, and Vinodkumar Prabhakaran. 2022 · 2022
Cited alongside, same era.
Language models (mostly) know what they know
Saurav Kadavath, Tom Conerly, Amanda Askell, Tom Henighan, Dawn Drain, Ethan Perez, Nicholas Schiefer, Zac Hatfield-Dodds, Nova DasSarma, Eli Tran-Johnson, et al. 2022 · 2022
Cited alongside, same era.
In defense of the unitary scalarization for deep multi-task learning
Vitaly Kurin, Alessandro De Palma, Ilya Kostrikov, Shimon Whiteson, and Pawan K Mudigonda. 2022 · 2022
Cited alongside, same era.
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, et al. 2022 · 2022
Cited alongside, same era.
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D Manning, and Chelsea Finn. 2023 · 2023
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Stanford alpaca: An instruction-following llama model
Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto. 2023 · 2023
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Katherine Tian, Eric Mitchell, Allan Zhou, Archit Sharma, Rafael Rafailov, Huaxiu Yao, Chelsea Finn, and Christopher D Manning. 2023 · 2023
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Everyone’s voice matters: Quantifying annotation disagreement using demographic information
Ruyuan Wan, Jaehyung Kim, and Dongyeop Kang. 2023 · 2023
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Aligning language models with human preferences via a bayesian approach
Jiashuo Wang, Haozhao Wang, Shichao Sun, and Wenjie Li. 2023 · 2023
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Rrhf: Rank responses to align language models with human feedback without tears
Zheng Yuan, Hongyi Yuan, Chuanqi Tan, Wei Wang, Songfang Huang, and Fei Huang. 2023 · 2023
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Blind image quality assessment via vision-language correspondence: A multitask learning perspective
Weixia Zhang, Guangtao Zhai, Ying Wei, Xiaokang Yang, and Kede Ma. 2023 · 2023
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Slic-hf: Sequence likelihood calibration with human feedback
Yao Zhao, Rishabh Joshi, Tianqi Liu, Misha Khalman, Mohammad Saleh, and Peter J Liu. 2023 · 2023
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Which samples should be learned first: Easy or hard?
Xiaoling Zhou and Ou Wu. 2023 · 2023
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Beyond one-preference-for-all: Multi-objective direct preference optimization
Zhanhui Zhou, Jie Liu, Chao Yang, Jing Shao, Yu Liu, Xiangyu Yue, Wanli Ouyang, and Yu Qiao. 2023 · 2023
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Controllable preference optimization: Toward controllable multi-objective alignment
Yiju Guo, Ganqu Cui, Lifan Yuan, Ning Ding, Jiexin Wang, Huimin Chen, Bowen Sun, Ruobing Xie, Jie Zhou, Yankai Lin, et al. 2024 · 2024
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Shed: Shapley-based automated dataset refinement for instruction fine-tuning
Yexiao He, Ziyao Wang, Zheyu Shen, Guoheng Sun, Yucong Dai, Yongkai Wu, Hongyi Wang, and Ang Li. 2024 · 2024
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Spo: Multi-dimensional preference sequential alignment with implicit reward modeling
Xingzhou Lou, Junge Zhang, Jian Xie, Lifeng Liu, Dong Yan, and Kaiqi Huang. 2024 · 2024
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Warm: On the benefits of weight averaged reward models
Alexandre Ramé, Nino Vieillard, Léonard Hussenot, Robert Dadashi, Geoffrey Cideron, Olivier Bachem, and Johan Ferret. 2024 · 2024
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Fine-grained human feedback gives better rewards for language model training
Zeqiu Wu, Yushi Hu, Weijia Shi, Nouha Dziri, Alane Suhr, Prithviraj Ammanabrolu, Noah A Smith, Mari Ostendorf, and Hannaneh Hajishirzi. 2024 · 2024
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