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High-quality preference datasets are essential for training reward models that can effectively guide large language models (LLMs) in generating high-quality responses aligned with human preferences.
Survey article: Inter-coder agreement for computational linguistics
Ron Artstein and Massimo Poesio · 2008
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Compositional semantic parsing on semi-structured tables
Panupong Pasupat and Percy Liang · 2015
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Enriching word vectors with subword information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov · 2017
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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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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Proximal policy optimization algorithms
John Schulman, Filip Wolski, Prafulla Dhariwal, Alec Radford, and Oleg Klimov · 2017
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Megatron-lm: Training multi-billion parameter language models using model parallelism, 2020
Mohammad Shoeybi, Mostofa Patwary, Raul Puri, Patrick LeGresley, Jared Casper, and Bryan Catanzaro · 2020
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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
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Finqa: A dataset of numerical reasoning over financial data
Zhiyu Chen, Wenhu Chen, Charese Smiley, Sameena Shah, Iana Borova, Dylan Langdon, Reema Moussa, Matt Beane, Ting-Hao Huang, Bryan Routledge, et al · 2021
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Webgpt: Browser-assisted question-answering with human feedback
Reiichiro Nakano, Jacob Hilton, Suchir Balaji, Jeff Wu, Long Ouyang, Christina Kim, Christopher Hesse, Shantanu Jain, Vineet Kosaraju, William Saunders, Xu Jiang, Karl Cobbe, Tyna Eloundou, Gretchen Krueger, Kevin Button, Matthew Knight, Benjamin Chess, and John Schulman · 2021
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Training language models to follow instructions with human feedback, 2022
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 Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe · 2022
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Training a helpful and harmless assistant with reinforcement learning from human feedback, 2022
Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, Ben Mann, and Jared Kaplan · 2022
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Bertopic: Neural topic modeling with a class-based tf-idf procedure
Maarten Grootendorst · 2022
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TruthfulQA: Measuring how models mimic human falsehoods
Stephanie Lin, Jacob Hilton, and Owain Evans · 2022
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Learning to summarize from human feedback, 2022
Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, and Paul Christiano · 2022
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Understanding dataset difficulty with 𝒱 \mathcal{V} -usable information
Kawin Ethayarajh, Yejin Choi, and Swabha Swayamdipta · 2022
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Understanding dataset difficulty with 𝒱 \mathcal{V} -usable information
Kawin Ethayarajh, Yejin Choi, and Swabha Swayamdipta · 2022
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Zephyr: Direct distillation of lm alignment, 2023
Lewis Tunstall, Edward Beeching, Nathan Lambert, Nazneen Rajani, Kashif Rasul, Younes Belkada, Shengyi Huang, Leandro von Werra, Clémentine Fourrier, Nathan Habib, Nathan Sarrazin, Omar Sanseviero, Alexander M. Rush, and Thomas Wolf · 2023
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Openassistant conversations – democratizing large language model alignment, 2023
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
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Helpsteer: Multi-attribute helpfulness dataset for steerlm, 2023
Zhilin Wang, Yi Dong, Jiaqi Zeng, Virginia Adams, Makesh Narsimhan Sreedhar, Daniel Egert, Olivier Delalleau, Jane Polak Scowcroft, Neel Kant, Aidan Swope, and Oleksii Kuchaiev · 2023
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Ultrafeedback: Boosting language models with high-quality feedback, 2023
Ganqu Cui, Lifan Yuan, Ning Ding, Guanming Yao, Wei Zhu, Yuan Ni, Guotong Xie, Zhiyuan Liu, and Maosong Sun · 2023
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Starling-7b: Improving LLM helpfulness & harmlessness with RLAIF, November 2023
Banghua Zhu, Evan Frick, Tianhao Wu, Hanlin Zhu, and Jiantao Jiao · 2023
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Steerlm: Attribute conditioned sft as an (user-steerable) alternative to rlhf, 2023
Yi Dong, Zhilin Wang, Makesh Narsimhan Sreedhar, Xianchao Wu, and Oleksii Kuchaiev · 2023
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RyokoAI/ShareGPT52K
RyokoAI · 2023
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#InsTag: Instruction tagging for analyzing supervised fine-tuning of large language models, 2023
Keming Lu, Hongyi Yuan, Zheng Yuan, Runji Lin, Junyang Lin, Chuanqi Tan, Chang Zhou, and Jingren Zhou · 2023
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NVIDIA AI foundation models: Build custom enterprise chatbots and co-pilots with production-ready LLMs, 2023
Vivienne Zhang, Shashank Verma, Neal Vaidya, Abhishek Sawarkar, and Amanda Saunders · 2023
Cited alongside, same era.
Direct preference optimization: Your language model is secretly a reward model, 2023
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, and Chelsea Finn · 2023
Cited alongside, same era.
Chatbot arena: An open platform for evaluating llms by human preference, 2024
Wei-Lin Chiang, Lianmin Zheng, Ying Sheng, Anastasios Nikolas Angelopoulos, Tianle Li, Dacheng Li, Hao Zhang, Banghua Zhu, Michael Jordan, Joseph E. Gonzalez, and Ion Stoica · 2024
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Rewardbench: Evaluating reward models for language modeling, 2024
Nathan Lambert, Valentina Pyatkin, Jacob Morrison, LJ Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, Noah A. Smith, and Hannaneh Hajishirzi · 2024
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Distilabel capybara
Argilla · 2024
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Nemotron-4 15b technical report, 2024
Jupinder Parmar, Shrimai Prabhumoye, Joseph Jennings, Mostofa Patwary, Sandeep Subramanian, Dan Su, Chen Zhu, Deepak Narayanan, Aastha Jhunjhunwala, Ayush Dattagupta, Vibhu Jawa, Jiwei Liu, Ameya Mahabaleshwarkar, Osvald Nitski, Annika Brundyn, James Maki, Miguel Martinez, Jiaxuan You, John Kamalu, Patrick LeGresley, Denys Fridman, Jared Casper, Ashwath Aithal, Oleksii Kuchaiev, Mohammad Shoeybi, Jonathan Cohen, and Bryan Catanzaro · 2024
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NeMo-Aligner: Scalable toolkit for efficient model alignment, 2024
Gerald Shen, Zhilin Wang, Olivier Delalleau, Jiaqi Zeng, Yi Dong, Daniel Egert, Shengyang Sun, Jimmy Zhang, Sahil Jain, Ali Taghibakhshi, Markel Sanz Ausin, Ashwath Aithal, and Oleksii Kuchaiev · 2024
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Judging llm-as-a-judge with mt-bench and chatbot arena, 2023
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
Cited alongside, same era.
The alignment ceiling: Objective mismatch in reinforcement learning from human feedback, 2023
Nathan Lambert and Roberto Calandra · 2023
Cited alongside, same era.
A long way to go: Investigating length correlations in rlhf, 2023
Prasann Singhal, Tanya Goyal, Jiacheng Xu, and Greg Durrett · 2023
Cited alongside, same era.
Enhancing chat language models by scaling high-quality instructional conversations
Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Zhi Zheng, Shengding Hu, Zhiyuan Liu, Maosong Sun, and Bowen Zhou · 2023
Cited alongside, same era.
Arb: Advanced reasoning benchmark for large language models
Tomohiro Sawada, Daniel Paleka, Alexander Havrilla, Pranav Tadepalli, Paula Vidas, Alexander Kranias, John J Nay, Kshitij Gupta, and Aran Komatsuzaki · 2023
Cited alongside, same era.
Scibench: Evaluating college-level scientific problem-solving abilities of large language models
Xiaoxuan Wang, Ziniu Hu, Pan Lu, Yanqiao Zhu, Jieyu Zhang, Satyen Subramaniam, Arjun R Loomba, Shichang Zhang, Yizhou Sun, and Wei Wang · 2023
Cited alongside, same era.
tigerbot-kaggle-leetcodesolutions-en-2k, 2023
TigerResearch · 2023
Cited alongside, same era.
Closest in time.
Cohen kappa score
Scikit-Learn · 2024
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RLHF workflow: From reward modeling to online RLHF, 2024
Hanze Dong, Wei Xiong, Bo Pang, Haoxiang Wang, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, and Tong Zhang · 2024
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Advancing LLM reasoning generalists with preference trees, 2024
Lifan Yuan, Ganqu Cui, Hanbin Wang, Ning Ding, Xingyao Wang, Jia Deng, Boji Shan, Huimin Chen, Ruobing Xie, Yankai Lin, Zhenghao Liu, Bowen Zhou, Hao Peng, Zhiyuan Liu, and Maosong Sun · 2024
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Reward bench leaderboard
Allen AI · 2024
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Prometheus 2: An open source language model specialized in evaluating other language models, 2024
Seungone Kim, Juyoung Suk, Shayne Longpre, Bill Yuchen Lin, Jamin Shin, Sean Welleck, Graham Neubig, Moontae Lee, Kyungjae Lee, and Minjoon Seo · 2024
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Length-controlled alpacaeval: A simple way to debias automatic evaluators
Yann Dubois, Balázs Galambosi, Percy Liang, and Tatsunori B Hashimoto · 2024
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From live data to high-quality benchmarks: The Arena-Hard pipeline
Tianle Li, Wei-Lin Chiang, Evan Frick, Lisa Dunlap, Banghua Zhu, Joseph E. Gonzalez, and Ion Stoica · 2024
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SimPO: Simple preference optimization with a reference-free reward, 2024
Yu Meng, Mengzhou Xia, and Danqi Chen · 2024
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Introducing Meta Llama 3: The most capable openly available LLM to date
Meta AI · 2024
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Brain: Bayesian reward-conditioned amortized inference for natural language generation from feedback, 2024
Gaurav Pandey, Yatin Nandwani, Tahira Naseem, Mayank Mishra, Guangxuan Xu, Dinesh Raghu, Sachindra Joshi, Asim Munawar, and Ramón Fernandez Astudillo · 2024
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Open llm leaderboard
HuggingFace · 2024
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Arena-hard-auto leaderboard
LMSys · 2024
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Welcome Llama 3 - Meta’s new open LLM
Philipp Schmid, Omar Sanseviero, Pedro Cuenca, Younes Belkada, and Leandro von Werra · 2024
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More rlhf, more trust? on the impact of human preference alignment on language model trustworthiness, 2024
Aaron J. Li, Satyapriya Krishna, and Himabindu Lakkaraju · 2024
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Secrets of rlhf in large language models part ii: Reward modeling, 2024
Binghai Wang, Rui Zheng, Lu Chen, Yan Liu, Shihan Dou, Caishuang Huang, Wei Shen, Senjie Jin, Enyu Zhou, Chenyu Shi, Songyang Gao, Nuo Xu, Yuhao Zhou, Xiaoran Fan, Zhiheng Xi, Jun Zhao, Xiao Wang, Tao Ji, Hang Yan, Lixing Shen, Zhan Chen, Tao Gui, Qi Zhang, Xipeng Qiu, Xuanjing Huang, Zuxuan Wu, and Yu-Gang Jiang · 2024
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Tenyxchat: Language model alignment using tenyx fine-tuning, 2024
Tenyx · 2024
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