--- title: "Who cites Black-Box Prompt Optimization: — 46 citing papers" description: "46 papers of the Elamur Research library cite «Black-Box Prompt Optimization: Aligning Large Language Models without Model Training»…" canonical: "https://research.elamur.ai/free/en/papers/cheng-2024-black-box-prompt-optimization-aligning/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/cheng-2024-black-box-prompt-optimization-aligning/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Black-Box Prompt Optimization: Aligning Large…](https://md.research.elamur.ai/free/en/papers/cheng-2024-black-box-prompt-optimization-aligning/) / cited by # Who cites «Black-Box Prompt Optimization: Aligning Large Language Models without Model Training» 46 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 «Black-Box Prompt Optimization: Aligning Large Language Models without Model Training»](https://md.research.elamur.ai/free/en/papers/cheng-2024-black-box-prompt-optimization-aligning/) 1. [**Unleashing the potential of prompt engineering for large language models arXiv:2310.14735 · Patterns 6(6) 101,260 (2025)**Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.*2023 · 536 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-unleashing-potential-prompt-engineering-large/) 2. **[ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search](https://md.research.elamur.ai/free/en/papers/zhang-2024-rest-mcts-llm-self-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Dan Zhang, Sining Zhoubian, Ziniu Hu, et al.*2024 · 200 citations* 3. **[AutoWebGLM: A Large Language Model-based Web Navigating Agent](https://md.research.elamur.ai/free/en/papers/lai-2024-autowebglm-large-language-model-based/) [ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD)](https://md.research.elamur.ai/free/en/venues/acm-sigkdd-conference-on-knowledge-discovery-and-data-mining-kdd/)**Hanyu Lai, Xiao Liu, Iat Long Iong, et al.*2024 · 96 citations* 4. **[PKU-SafeRLHF: Towards Multi-Level Safety Alignment for LLMs with Human Preference](https://md.research.elamur.ai/free/en/papers/ji-2025-pku-saferlhf-multi-level-safety/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiaming Ji, Donghai Hong, Borong Zhang, et al.*2025 · 83 citations* 5. **[Aligner: Efficient Alignment by Learning to Correct](https://md.research.elamur.ai/free/en/papers/ji-2024-aligner-efficient-alignment-learning-correct/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jiaming Ji, Boyuan Chen, Hantao Lou, et al.*2024 · 69 citations* 6. **[Agent-Pro: Learning to Evolve via Policy-Level Reflection and Optimization](https://md.research.elamur.ai/free/en/papers/zhang-2024-agent-pro-learning-evolve-policy/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenqi Zhang, Ke Tang, Hai Wu, et al.*2024 · 52 citations* 7. **[A Systematic Survey of Automatic Prompt Optimization Techniques](https://md.research.elamur.ai/free/en/papers/ramnath-2025-systematic-survey-automatic-prompt-optimization/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kiran Ramnath, Kang Zhou, Sheng Guan, et al.*2025 · 40 citations* 8. **[FaithLM: Towards Faithful Explanations for Large Language Models](https://md.research.elamur.ai/free/en/papers/chuang-2026-faithlm-faithful-explanations-large-language/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, et al.*2026 · 9 citations* 9. **[Self-Instructed Derived Prompt Generation Meets In-Context Learning: Unlocking New Potential of Black-Box LLMs](https://md.research.elamur.ai/free/en/papers/li-2025-self-instructed-derived-prompt-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhuo Li, Yuhao Du, Jinpeng Hu, et al.*2025 · 9 citations* 10. **[Rethinking Prompt Optimizers: From Prompt Merits to Optimization](https://md.research.elamur.ai/free/en/papers/zhu-2026-rethinking-prompt-optimizers-prompt-merits/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Zixiao Zhu, Hanzhang Zhou, Zijian Feng, et al.*2026 · 3 citations* 11. **[LLM Prompt Duel Optimizer: Efficient Label-Free Prompt Optimization](https://md.research.elamur.ai/free/en/papers/wu-2026-llm-prompt-duel-optimizer-efficient/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yuanchen Wu, Saurabh Verma, Justin Lee, et al.*2026 · 2 citations* 12. **[promptolution: A Unified, Modular Framework for Prompt Optimization](https://md.research.elamur.ai/free/en/papers/zehle-2026-promptolution-unified-modular-framework-prompt/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Tom Zehle, Timo Heiß, Moritz Schlager, et al.*2026 · 2 citations* 13. **[When Prompt Optimization Becomes Jailbreaking: Adaptive Red-Teaming of Large Language Models](https://md.research.elamur.ai/free/en/papers/shamsi-2026-when-prompt-optimization-becomes-jailbreaking/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Zafir Shamsi, Nikhil Chekuru, Zachary Guzman, et al.*2026* 33 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/cheng-2024-black-box-prompt-optimization-aligning/) — Black-Box Prompt Optimization: Aligning Large Language Models without Model Training