The Death and Life of Great Prompts: Analyzing the Evolution of LLM Prompts from the Structural Perspective (2024.emnlp-main)
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| Challenge: | Recent research has shown that high-quality prompts are essential for LLMs to produce accurate and relevant responses. |
| Approach: | They analyze 10,538 in-the-wild prompts collected from various platforms and develop a framework that decomposes the prompts into eight key components. |
| Outcome: | The proposed framework decomposes 10,538 in-the-wild prompts into eight components. |
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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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What Makes a Good Natural Language Prompt? (2025.acl-long)
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| Challenge: | Existing studies on prompt quality show imbalanced support across models and tasks, and research gaps. |
| Approach: | They propose a property- and human-centric framework for evaluating prompt quality . they propose comparing prompt quality to other factors such as adverbs and apverbs . |
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PrExMe! Large Scale Prompt Exploration of Open Source LLMs for Machine Translation and Summarization Evaluation (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) are useful for low-resource scenarios and time-restricted applications. |
| Approach: | They propose a large-scale evaluation tool for large language models that uses prompts . they evaluate 720 prompt templates for open-source LLM-based metrics on MT and summarization datasets a 6.6M evaluations. |
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Deconstructing In-Context Learning: Understanding Prompts via Corruption (2024.lrec-main)
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| Challenge: | Prior work examined how modifying different elements of the prompt can affect model performance, but this limited number of elements made replication challenging. |
| Approach: | They decompose the entire prompt into four components: task description, demonstration inputs, labels, and inline instructions provided for each demonstration. |
| Outcome: | The proposed model is robust to minor prompt modifications, but its underlying pre-trained backbone is brittle . previous studies focused on models with fewer than 15 billion parameters or exclusively examined black-box models like GPT-3 or PaLM, making replication challenging. |
Prompt Compression for Large Language Models: A Survey (2025.naacl-long)
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| Challenge: | Current methods for improving LLM efficiency focus on optimizing the model itself, while prompt-centric methods focus on lowering the complexity of input. |
| Approach: | They propose to use prompt compression to optimize the compression encoder and combine hard and soft prompt methods to improve the efficiency of LLMs. |
| Outcome: | The proposed methods are categorized into hard prompt methods and soft prompt methods. |
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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PromptPrism: A Linguistically-Inspired Taxonomy for Prompts (2026.findings-eacl)
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| Challenge: | PromptPrism is a linguistically-inspired taxonomy that enables prompt analysis across three hierarchical levels. |
| Approach: | They propose a linguistically-inspired taxonomy that enables prompt analysis across three hierarchical levels: functional structure, semantic component, and syntactic pattern. |
| Outcome: | The proposed taxonomy bridges traditional language understanding with modern LLM research . it improves prompt quality and improves model performance across tasks . |
DLPO: Towards a Robust, Efficient, and Generalizable Prompt Optimization Framework from a Deep-Learning Perspective (2025.findings-emnlp)
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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. |
| Outcome: | The proposed methods integrate deep learning paradigms into text-based gradient optimization. |
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. |
Are All Prompt Components Value-Neutral? Understanding the Heterogeneous Adversarial Robustness of Dissected Prompt in LLMs (2026.eacl-long)
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Yujia Zheng, Tianhao Li, Haotian Huang, Tianyu Zeng, Jingyu Lu, Chuangxin Chu, Yuekai Huang, Ziyou Jiang, Qian Xiong, Yuyao Ge, Mingyang Li
| Challenge: | Existing studies treat prompts as flat text, overlooking their internal structure, and different components within a prompt contribute unequally to robustness. |
| Approach: | They propose a framework that decomposes prompts into functional components and a method that selectively modifies components to expose component-wise vulnerabilities. |
| Outcome: | The proposed framework exposes component-wise vulnerabilities while ensuring linguistic plausibility through perplexity-based filtering. |