Fetching the paper…
Reading the bibliography…
Inference-time computation is a powerful paradigm to enhance the performance of large language models (LLMs), with Best-of-N sampling being a widely used technique.
Rank analysis of incomplete block designs: I. the method of paired comparisons
Ralph Allan Bradley and Milton E. Terry · 1952
Earlier work this paper cites.
Coarse-to-fine n-best parsing and maxent discriminative reranking
Eugene Charniak and Mark Johnson · 2005
Earlier work this paper cites.
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 F. Christiano · 2009
Earlier work this paper cites.
Training verifiers to solve math word problems, 2021
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman · 2021
Earlier work this paper cites.
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
Earlier work this paper cites.
Direct preference-based policy optimization without reward modeling, 2023
Gaon An, Junhyeok Lee, Xingdong Zuo, Norio Kosaka, Kyung-Min Kim, and Hyun Oh Song · 2023
Earlier work this paper cites.
A General Theoretical Paradigm to Understand Learning from Human Preferences
Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, Daniel Guo, Daniele Calandriello, Michal Valko, and Rémi Munos · 2023
Earlier work this paper cites.
Self-refine: Iterative refinement with self-feedback, 2023
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, Shashank Gupta, Bodhisattwa Prasad Majumder, Katherine Hermann, Sean Welleck, Amir Yazdanbakhsh, and Peter Clark · 2023
Cited alongside, same era.
Direct preference optimization: Your language model is secretly a reward model
Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D Manning, and Chelsea Finn · 2023
Cited alongside, same era.
Self-consistency improves chain of thought reasoning in language models, 2023
Xuezhi Wang, Jason Wei, Dale Schuurmans, Quoc Le, Ed Chi, Sharan Narang, Aakanksha Chowdhery, and Denny Zhou · 2023
Cited alongside, same era.
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
The llama 3 herd of models, 2024
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, and et al · 2024
Closest in time.
Alpacafarm: A simulation framework for methods that learn from human feedback, 2024
Yann Dubois, Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy Liang, and Tatsunori B. Hashimoto · 2024
Closest in time.
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
Closest in time.
Scaling llm test-time compute optimally can be more effective than scaling model parameters, 2024
Charlie Snell, Jaehoon Lee, Kelvin Xu, and Aviral Kumar · 2024
Closest in time.
Interpretable preferences via multi-objective reward modeling and mixture-of-experts, 2024
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Critique-out-loud reward models, 2024
Zachary Ankner, Mansheej Paul, Brandon Cui, Jonathan D. Chang, and Prithviraj Ammanabrolu · 2024
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
Cited alongside, same era.
Haoxiang Wang, Wei Xiong, Tengyang Xie, Han Zhao, and Tong Zhang · 2024
Closest in time.
Generative verifiers: Reward modeling as next-token prediction, 2024
Lunjun Zhang, Arian Hosseini, Hritik Bansal, Mehran Kazemi, Aviral Kumar, and Rishabh Agarwal · 2024
Closest in time.