| Challenge: | Prompt engineering is done manually in a trial-and-error ad-hoc fashion, authors say . |
| Approach: | They propose a method to rewrite an under-optimized prompt to a more effective prompt. |
| Outcome: | The proposed method rewrites an under-optimized prompt to a more effective prompt. |
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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
| Challenge: | Recent advances in prompt engineering have created impediments for end users to adopt . however, prompt engineering remains an impedance due to rapid advances in models, tasks, and associated best practices. |
| Approach: | They propose to define APO as a 5-part unifying framework and categorize all relevant works based on their salient features. |
| Outcome: | The proposed framework aims to improve the performance of large language models on various tasks. |
Prompt-R1: Collaborative Automatic Prompting Framework via End-to-end Reinforcement Learning (2026.findings-acl)
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Wenjin Liu, Haoran Luo, Xueyuan Lin, Haoming Liu, Tiesunlong Shen, Jiapu Wang, Rui Mao, Erik Cambria
| Challenge: | Existing large language models are limited in understanding, reasoning, calculation, and generation, limiting their performance in complex reasoning and dynamic tasks. |
| Approach: | They propose a plug-and-play framework that integrates a small-scale LLM (as agent) with large-scale large-level LLMs (a as environment) they propose generating prompts that are used to interact with LLM, and a double constraint reward that optimizes correctness and quality of generation. |
| Outcome: | The proposed framework significantly outperforms baseline large-scale large-language models across various tasks. |
AutoDSPy: Automating Modular Prompt Design with Reinforcement Learning for Small and Large Language Models (2025.emnlp-industry)
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Nafew Azim, Abrar Ur Alam, Hasan Bin Omar, Abdullah Mohammad Muntasir Adnan Jami, Jawad Ibn Ahad, Muhammad Rafsan Kabir, Md. Ismail Hossain, Fuad Rahman, Mohammad Ruhul Amin, Shafin Rahman, Nabeel Mohammed
| Challenge: | Large Language Models excel at complex reasoning tasks, yet their performance hinges on the quality of their prompts and pipeline structures. |
| Approach: | They propose a framework that fully automates large language models' pipeline construction using reinforcement learning. |
| Outcome: | Experimental results show that autoDSPy outperforms DSPy benchmarks in accuracy gains and time. |
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. |
| Approach: | They propose a model-adaptive prompt optimizer method that optimizes original prompts for each LLM in downstream tasks. |
| Outcome: | The proposed method can optimize prompts for an LLM in downstream tasks. |
ADO: Automatic Data Optimization for Inputs in LLM Prompts (2025.findings-acl)
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| Challenge: | Recent research has focused on refining instruction components and augmenting input data with in-context examples, but this study explores the potential benefits of optimizing the input data itself. |
| Approach: | They propose a content engineering and structural reformulation strategy to optimize input data within prompts to improve performance of Large Language Models. |
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SOPL: A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models (2025.findings-emnlp)
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| Challenge: | Using automated prompt engineering to identify effective features is essential for large language models. |
| Approach: | They propose an optimal learning framework for automated prompt engineering for black-box models . feature-based method is used to express prompt templates, which broadens the search space . |
| Outcome: | The proposed learning framework outperforms benchmark strategies on instruction induction tasks with limited budgets. |
Instances Need More Care: Rewriting Prompts for Instances with LLMs in the Loop Yields Better Zero-Shot Performance (2024.findings-acl)
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| Challenge: | Large language models (LLMs) have revolutionized zero-shot task performance, mitigating the need for task-specific annotations while enhancing task generalizability. |
| Approach: | They propose an approach that optimizes the zero-shot prompts for individual task instances following an innovative manner of "LLMs in the loop" their results show that PRomPTed outperforms naive zero- shot approaches and a strong baseline which refines the task output instead of the input prompt. |
| Outcome: | The proposed approach outperforms naive approaches and a strong baseline which refines the task output instead of the input prompt. |
CRL-Prompt: Contrastive and Reinforcement Learning for Soft Prompt Tuning for Text Classification (2026.acl-srw)
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| Challenge: | Manual prompt engineering is time-consuming, non-scalable, and brittle, while current auto-prompting techniques are far from maturity. |
| Approach: | They propose a two-stage method for prompt learning of frozen language models, CRL-Prompt, based on soft prompt initialization followed by contrastive and reinforcement-based refinement. |
| Outcome: | The proposed method achieves consistent improvements over baseline prompt tuning strategies, with gains of up to 2.2% while training fewer than 0.25% of model parameters. |
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. |
| Outcome: | The proposed framework improves prompt quality across 45 tasks and reduces API calls, token usage and overall cost. |
Heuristic-based Search Algorithm in Automatic Instruction-focused Prompt Optimization: A Survey (2025.findings-acl)
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Wendi Cui, Jiaxin Zhang, Zhuohang Li, Hao Sun, Damien Lopez, Kamalika Das, Bradley A. Malin, Sricharan Kumar
| Challenge: | Recent advances in Large Language Models (LLMs) have led to remarkable achievements across a variety of NLP tasks. |
| Approach: | They propose a taxonomy of automatic prompt optimization methods that explore and improve prompts with minimal human oversight. |
| Outcome: | The proposed methods can explore and improve prompts with minimal human oversight. |