--- title: "Who cites Aligning Language Models with — 34 citing papers" description: "34 papers of the Elamur Research library cite «Aligning Language Models with Preferences through f-divergence Minimization», found in their…" canonical: "https://research.elamur.ai/free/en/papers/go-2023-aligning-language-models-preferences-through/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/go-2023-aligning-language-models-preferences-through/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Aligning Language Models with Preferences through…](https://md.research.elamur.ai/free/en/papers/go-2023-aligning-language-models-preferences-through/) / cited by # Who cites «Aligning Language Models with Preferences through f-divergence Minimization» 34 papers of our library cite it (of the 149,439 whose bibliographies we read) · 12 with a page here, the most cited first · [← back to «Aligning Language Models with Preferences through f-divergence Minimization»](https://md.research.elamur.ai/free/en/papers/go-2023-aligning-language-models-preferences-through/) 1. [**Direct Preference Optimization: Your Language Model is Secretly a Reward Model arXiv:2305.18290**Rafael Rafailov, Archit Sharma, Eric Mitchell, et al.*2023 · 2,896 citations*](https://md.research.elamur.ai/free/en/papers/rafailov-2023-direct-preference-optimization-language-model/) 2. [**Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation arXiv:2310.06987**Yangsibo Huang, Samyak Gupta, Mengzhou Xia, et al.*2023 · 169 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-catastrophic-jailbreak-open-source-llms/) 3. **[Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction Strategies](https://md.research.elamur.ai/free/en/papers/pan-2024-automatically-correcting-large-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Liangming Pan, Michael Saxon, Wenda Xu, et al.*2024 · 140 citations* 4. [**Large Language Model Alignment: A Survey arXiv:2309.15025**Tianhao Shen, Renren Jin, Yufei Huang, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/) 5. [**Pretraining Language Models with Human Preferences arXiv:2302.08582**Tomasz Korbak, Kejian Shi, Angelica Chen, et al.*2023 · 121 citations*](https://md.research.elamur.ai/free/en/papers/korbak-2023-pretraining-language-models-human-preferences/) 6. **[Defending Against Alignment-Breaking Attacks via Robustly Aligned LLM](https://md.research.elamur.ai/free/en/papers/cao-2024-defending-against-alignment-breaking-attacks/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bochuan Cao, Yuanpu Cao, Lu Lin, et al.*2024 · 109 citations* 7. **[Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewards](https://md.research.elamur.ai/free/en/papers/rame-2023-rewarded-soups-towards-pareto-optimal/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Alexandre Rame, Guillaume Couairon, Mustafa Shukor, et al.*2023 · 90 citations* 8. **[The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts](https://md.research.elamur.ai/free/en/papers/shen-2024-language-barrier-dissecting-safety-challenges/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lingfeng Shen, Weiting Tan, Sihao Chen, et al.*2024 · 88 citations* 9. [**QuRating: Selecting High-Quality Data for Training Language Models arXiv:2402.09739**Alexander Wettig, Aatmik Gupta, Saumya Malik, et al.*2024 · 74 citations*](https://md.research.elamur.ai/free/en/papers/wettig-2024-qurating-selecting-high-quality-data/) 10. **[Online Difficulty Filtering for Reasoning Oriented Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/bae-2026-online-difficulty-filtering-reasoning-oriented/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Sanghwan Bae, Jiwoo Hong, Min Young Lee, et al.*2026 · 22 citations* 11. [**The Trickle-down Impact of Reward (In-)consistency on RLHF arXiv:2309.16155**Lingfeng Shen, Sihao Chen, Linfeng Song, et al.*2023 · 11 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-trickle-down-impact-reward-consistency/) 12. [**Exploring the impact of low-rank adaptation on the performance, efficiency, and regularization of RLHF arXiv:2309.09055**Simeng Sun, Dhawal Gupta, Mohit Iyyer*2023 · 9 citations*](https://md.research.elamur.ai/free/en/papers/sun-2023-exploring-impact-low-rank-adaptation/) 22 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/go-2023-aligning-language-models-preferences-through/) — Aligning Language Models with Preferences through f-divergence Minimization