🔗 Who cites «Nash Learning from Human Feedback»

31 papers of our library cite it (of the 149,439 whose bibliographies we read) · 13 with a page here, the most cited first · ← back to «Nash Learning from Human Feedback»

1. Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-ExpertsFindings of the Association for Computational LinguisticsHaoxiang Wang, Wei Xiong, Tengyang Xie, et al.2024 · 153 citations
2. SaulLM-7B: A pioneering Large Language Model for LawarXiv:2403.03883Pierre Colombo, Telmo Pessoa Pires, Malik Boudiaf, et al.2024 · 119 citations
3. A Survey of Reinforcement Learning from Human FeedbackTransactions on Machine Learning ResearchTimo Kaufmann, Paul Weng, Viktor Bengs, et al.2023 · 92 citations
4. Arithmetic Control of LLMs for Diverse User Preferences: Directional Preference Alignment with Multi-Objective RewardsAnnual Meeting of the Association for Computational Linguistics (ACL)Haoxiang Wang, Yong Lin, Wei Xiong, et al.2024 · 68 citations
5. Generative Reward ModelsarXiv:2410.12832Dakota Mahan, Duy Van Phung, Rafael Rafailov, et al.2024 · 58 citations
6. RL on Incorrect Synthetic Data Scales the Efficiency of LLM Math Reasoning by Eight-FoldConference on Neural Information Processing Systems (NeurIPS)Amrith Setlur, Saurabh Garg, Xinyang Geng, et al.2024 · 56 citations
7. Critique-out-Loud Reward ModelsarXiv:2408.11791Zachary Ankner, Mansheej Paul, Brandon Cui, et al.2024 · 52 citations
8. Easy-to-Hard Generalization: Scalable Alignment Beyond Human SupervisionConference on Neural Information Processing Systems (NeurIPS)Zhiqing Sun, Longhui Yu, Yikang Shen, et al.2024 · 51 citations
9. Direct Nash Optimization: Teaching Language Models to Self-Improve with General PreferencesarXiv:2404.03715Corby Rosset, Ching-An Cheng, Arindam Mitra, et al.2024 · 44 citations
10. LLM-as-a-Judge & Reward Model: What They Can and Cannot DoarXiv:2409.11239Guijin Son, Hyunwoo Ko, Hoyoung Lee, et al.2024 · 27 citations
11. BOND: Aligning LLMs with Best-of-N DistillationarXiv:2407.14622Pier Giuseppe Sessa, Robert Dadashi, Leonard Hussenot, et al.2024 · 23 citations
12. Beyond Compromise: Pareto-Lenient Consensus for Efficient Multi-Preference LLM AlignmentFindings of the Association for Computational LinguisticsRenxuan Tan, Rongpeng Li, Zhifeng Zhao, et al.2026 · 1 citation
13. Mitigating Cultural Bias in LLMs via Multi-Agent Cultural DebateFindings of the Association for Computational LinguisticsQian Tan, Lei Jiang, Yuting Zeng, et al.2026

18 more papers of our library cite it; they have no page here yet.

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