Shanu Kumar, Akhila Yesantarao Venkata, Shubhanshu Khandelwal, Bishal Santra, Parag Agrawal, Manish Gupta
| Challenge: | Existing methods for prompt optimization struggle with longer, more complex ones, often risking information loss and being sensitive to small perturbations. |
| Approach: | They propose a framework that treats prompt optimization as a hierarchical tree refinement problem and uses a Critic-Actor framework to generate reflections and apply actions to refine the prompt. |
| Outcome: | The proposed framework produces more stable and interpretable prompt modifications, ensuring better generalization across tasks. |
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| Challenge: | Existing methods for prompt optimization still face challenges in robustness, efficiency, and generalization. |
| Approach: | They propose 7 new approaches inspired by traditional deep learning paradigms for prompt optimization that integrate text-based gradient 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. |
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| Challenge: | Existing prompt optimization methods often underperform due to learning exclusively from incorrect samples. |
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Beyond Prompt Engineering: A Systematic Analysis of Prompt Lexical Sensitivity and Its Impacts on Quality (2026.findings-acl)
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Qipeng Xie, Zi Liang, Jiafei Wu, Yufei Chen, Weizheng Wang, Wenao Ma, Zhong Ming, Haiqin Yang, Kaishun Wu
| Challenge: | Existing studies on prompt engineering have focused on optimizing models for performance under stylistic perturbations. |
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| Challenge: | Existing approaches to debiase MLLMs rely on handcrafted prompts that are brittle and difficult to generalize across tasks and bias types. |
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PromptIntern: Saving Inference Costs by Internalizing Recurrent Prompt during Large Language Model Fine-tuning (2024.findings-emnlp)
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| Challenge: | Recent advances in fine-tuning large language models have greatly enhanced their usage in domain-specific tasks. |
| Approach: | They propose a method which internalizes prompt knowledge during model fine-tuning to achieve efficient inference and save costs. |
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XPrompt: Exploring the Extreme of Prompt Tuning (2022.emnlp-main)
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| Challenge: | Prompt tuning learns soft prompts to condition pre-trained Language Models for performing downstream tasks in a parameter-efficient manner. |
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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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| Challenge: | Large language models (LLMs) have demonstrated strong performance across diverse tasks, but their performance varies significantly across different prompts. |
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Rethinking Prompt Optimizers: From Prompt Merits to Optimization (2026.eacl-long)
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Zixiao Zhu, Hanzhang Zhou, Zijian Feng, Tianjiao Li, Chua Jia Jim Deryl, Lee Onn Mak, Gee Wah Ng, Kezhi Mao
| Challenge: | Existing methods to optimize prompts rely on LLMs' self-generation ability but lack interpretability due to implicit optimization. |
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