| Challenge: | Existing prompt optimization methods optimize prompts globally, but they lack the correct words for a task. |
| Approach: | They propose a local prompt optimization process that integrates with any general automatic prompt engineering method to optimize a prompt over a large vocabulary. |
| Outcome: | The proposed method improves on Math Reasoning and BIG-bench Hard benchmarks and shows that it can converge to the optimal prompt faster than global methods. |
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Jinyu Xiang, Jiayi Zhang, Zhaoyang Yu, Xinbing Liang, Fengwei Teng, Jinhao Tu, Fashen Ren, Xiangru Tang, Sirui Hong, Chenglin Wu, Yuyu Luo
| Challenge: | Existing prompt optimization methods rely heavily on external references such as ground truth or by humans, limiting their applicability in real-world scenarios where such data is unavailable or costly to obtain. |
| Approach: | They propose a cost-efficient framework that discovers effective prompts for both closed and open-ended tasks without external reference. |
| Outcome: | The proposed framework outperforms state-of-the-art prompt optimization methods with significantly lower costs and fewer samples. |
MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization (2023.findings-emnlp)
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Yuyan Chen, Zhihao Wen, Ge Fan, Zhengyu Chen, Wei Wu, Dayiheng Liu, Zhixu Li, Bang Liu, Yanghua Xiao
| Challenge: | Existing research emphasizes the importance of adapting prompts to specific tasks, rather than specific LLMs. |
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SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization (2025.acl-long)
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Wendi Cui, Jiaxin Zhang, Zhuohang Li, Hao Sun, Damien Lopez, Kamalika Das, Bradley A. Malin, Sricharan Kumar
| Challenge: | Existing approaches separate the optimization of prompt instructions and in-context learning examples, leading to incohesive, suboptimal results. |
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A Systematic Survey of Automatic Prompt Optimization Techniques (2025.emnlp-main)
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Kiran Ramnath, Kang Zhou, Sheng Guan, Soumya Smruti Mishra, Xuan Qi, Zhengyuan Shen, Shuai Wang, Sangmin Woo, Sullam Jeoung, Yawei Wang, Haozhu Wang, Han Ding, Yuzhe Lu, Zhichao Xu, Yun Zhou, Balasubramaniam Srinivasan, Qiaojing Yan, Yueyan Chen, Haibo Ding, Panpan Xu, Lin Lee Cheong
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| Outcome: | The proposed framework aims to improve the performance of large language models on various tasks. |
promptolution: A Unified, Modular Framework for Prompt Optimization (2026.eacl-demo)
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| Challenge: | Existing implementations of prompt optimization are tied to unmaintained, isolated codebases or require invasive integration into application frameworks. |
| Approach: | They propose a unified, modular open-source framework that integrates multiple contemporary discrete prompt optimizers within a single extensible system for both practitioners and researchers. |
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Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models (2024.findings-naacl)
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| Challenge: | Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions. |
| Approach: | They propose a new approach that uses text embeddings to obtain basis vectors by matrix decomposition and constructs a space for representing all prompts. |
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PromptWizard: Optimizing Prompts via Task-Aware, Feedback-Driven Self-Evolution (2025.findings-acl)
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| Challenge: | Large language models (LLMs) have transformed AI across diverse domains, with prompting being central to their success in guiding model outputs. |
| Approach: | They propose a framework for discrete prompt optimization that generates human-readable prompts using feedback-driven critique and synthesis process. |
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PRompt Optimization in Multi-Step Tasks (PROMST): Integrating Human Feedback and Heuristic-based Sampling (2024.emnlp-main)
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| Challenge: | Prompt optimization aims to find the best prompt to a large language model (LLM) for a given task. |
| Approach: | They propose a method to optimize prompts for LLM-driven multi-step tasks using a human-designed feedback rule. |
| Outcome: | The proposed method outperforms human-engineered prompts and several other prompt optimization methods on 11 representative multi-step tasks. |
P3: Prompts Promote Prompting (2025.findings-acl)
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| Challenge: | Recent advances in prompt optimization have shown effectiveness of using multiple components to optimize models . however, such unilateral approaches often yield suboptimal results due to interdependent nature of these components. |
| Approach: | They propose a self-improvement framework that optimizes both system and user prompts . they use offline optimized prompts to promote online prompt optimization . |
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HIPO: A Hierarchical Prompt Optimization Framework with Task Awareness and Fine-Grained Debugging (2026.findings-acl)
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| Challenge: | Existing methods for prompt optimization apply the same prompt across all samples . existing methods ignore variation in sample difficulty . |
| Approach: | They propose a framework that shifts the paradigm from dataset-level to sample-level optimization. |
| Outcome: | The proposed framework outperforms baselines on 27 tasks and reduces API calls, token consumption and overall cost by 1.2 to 80. |