🔗 Who cites «EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers»

77 papers of our library cite it (of the 149,439 whose bibliographies we read) · 23 with a page here, the most cited first · ← back to «EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers»

1. A Survey of Large Language ModelsFrontiers of Computer ScienceWayne Xin Zhao, Kun Zhou, Junyi Li, et al.2023 · 2,875 citations
2. Unleashing the potential of prompt engineering for large language modelsarXiv:2310.14735 · Patterns 6(6) 101,260 (2025)Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.2023 · 536 citations
3. Large Language Models as OptimizersarXiv:2309.03409Chengrun Yang, Xuezhi Wang, Yifeng Lu, et al.2023 · 413 citations
4. DSPy: Compiling Declarative Language Model Calls into Self-Improving PipelinesarXiv:2310.03714Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, et al.2023 · 285 citations
5. PromptAgent: Strategic Planning with Language Models Enables Expert-level Prompt OptimizationarXiv:2310.16427Xinyuan Wang, Chenxi Li, Zhen Wang, et al.2023 · 102 citations
6. Optimizing Instructions and Demonstrations for Multi-Stage Language Model ProgramsConference on Empirical Methods in Natural Language Processing (EMNLP)Krista Opsahl-Ong, Michael J Ryan, Josh Purtell, et al.2024 · 99 citations
7. Agent-Pro: Learning to Evolve via Policy-Level Reflection and OptimizationAnnual Meeting of the Association for Computational Linguistics (ACL)Wenqi Zhang, Ke Tang, Hai Wu, et al.2024 · 52 citations
8. Automatic Instruction Evolving for Large Language ModelsConference on Empirical Methods in Natural Language Processing (EMNLP)Weihao Zeng, Can Xu, Yingxiu Zhao, et al.2024 · 28 citations
9. In-Context Principle Learning from MistakesarXiv:2402.05403Tianjun Zhang, Aman Madaan, Luyu Gao, et al.2024 · 19 citations
10. Zero-Shot Chain-of-Thought Reasoning Guided by Evolutionary Algorithms in Large Language ModelsLecture notes in computer scienceFeihu Jin, Yifan Liu, Ying Tan2024 · 18 citations
11. Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTSFindings of the Association for Computational LinguisticsShijia Xu, Zhou Wu, Xiaolong Jia, et al.2026 · 15 citations
12. Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language ModelsFindings of the Association for Computational LinguisticsYuan Sui, Yufei He, Tri Cao, et al.2026 · 13 citations
13. FaithLM: Towards Faithful Explanations for Large Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, et al.2026 · 9 citations
14. EVOTOOL: Self-Evolving Tool-Use Policy Optimization in LLM Agents via Blame-Aware Mutation and Diversity-Aware SelectionAnnual Meeting of the Association for Computational Linguistics (ACL)Shuo Yang, Caren Han, Xueqi Ma, et al.2026 · 6 citations
15. Detecting Training Data of Large Language Models via Expectation MaximizationConference of the European Chapter of the Association for Computational Linguistics (EACL)Gyuwan Kim, Yang Li, Evangelia Spiliopoulou, et al.2026 · 5 citations
16. MAPRO: Recasting Multi-Agent Prompt Optimization as Maximum a Posteriori InferenceFindings of the Association for Computational LinguisticsZheyuan Zhang, Lin Ge, Hongjiang Li, et al.2026 · 3 citations
17. LLM Prompt Duel Optimizer: Efficient Label-Free Prompt OptimizationFindings of the Association for Computational LinguisticsYuanchen Wu, Saurabh Verma, Justin Lee, et al.2026 · 2 citations
18. What Makes an LLM a Good Optimizer? A Trajectory Analysis of LLM-Guided Evolutionary SearchFindings of the Association for Computational LinguisticsXinhao Zhang, Xi Chen, François Portet, et al.2026 · 1 citation
19. Adaptive Prompt Optimization for Open-Ended Tasks: Uncertainty Preference as a Secondary SignalFindings of the Association for Computational LinguisticsShuyang Zhang, Zhixuan Liu, Zhichen Dong, et al.2026
20. Beyond Explicit Refusals: Soft-Failure Attacks on Retrieval-Augmented GenerationAnnual Meeting of the Association for Computational Linguistics (ACL)Wentao Zhang, Yan Zhuang, ZhuHang Zheng, et al.2026
21. Protecting Language Models Against Unauthorized Distillation through Trace RewritingAnnual Meeting of the Association for Computational Linguistics (ACL)Xinhang Ma, William Yeoh, Ning Zhang, et al.2026
22. Reasoning as Gradient: Scaling MLE Agents Beyond Tree SearchFindings of the Association for Computational LinguisticsYifei Zhang, Xu Yang, Xiao Yang, et al.2026
23. To Write or to Automate Linguistic Prompts, That Is the QuestionAnnual Conference of the European Association for Machine Translation (EAMT)Marina Sánchez-Torrón, Daria Akselrod, Jason Rauchwerk2026

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

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