| Challenge: | Existing studies have focused on algorithms, reward shaping, and data curation, but prior prompt engineering is understudied. |
| Approach: | They investigate prior prompt engineering (pPE) in reinforcement fine-tuning . they translate five representative iPE strategies into corresponding pPE approaches . |
| Outcome: | The proposed approaches outperform iPE-prompted models on in-domain and out-of-domain benchmarks. |
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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. |
Sample Design Engineering: An Empirical Study on Designing Better Fine-Tuning Samples for Information Extraction with LLMs (2024.emnlp-industry)
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| Challenge: | Prompt Engineering (PE) is renowned for improving IE performance through prompt modifications, but the realm of sample design for downstream fine-tuning remains unexplored. |
| Approach: | They propose a methodical approach to enhancing LLMs’ post-tuning performance by refining input, output, and reasoning designs. |
| Outcome: | The proposed approach outperforms heuristic design strategies on three complex IE tasks with four additional LLMs. |
PRewrite: Prompt Rewriting with Reinforcement Learning (2024.acl-short)
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| 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. |
How Does In-Context Learning Help Prompt Tuning? (2024.findings-eacl)
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| Challenge: | a growing number of parameter-efficient adaptation methods are needed to fine-tune large language models. |
| Approach: | They propose a method that combines prompt tuning and in-context learning to improve prompt tuning by concatenating a natural language demonstration with learned prompt embeddings. |
| Outcome: | The proposed method outperforms prompt tuning and prompt tuning on five language generation tasks. |
Exploring the Effectiveness of Prompt Engineering for Legal Reasoning Tasks (2023.findings-acl)
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| Challenge: | Recent studies have shown that Chain-of-Thought (CoT) prompts improve tasks such as arithmetic and common-sense reasoning. |
| Approach: | They evaluate CoT prompts and various prompting strategies for legal reasoning tasks . they find that the best results are achieved with prompts derived from specific legal reasoning techniques . |
| Outcome: | The proposed approaches improve the COLIEE entailment task on the Japanese bar exam . the proposed approaches surpass the best system from 2022 with an accuracy of 0.789 . |
Prompt Tuning for Discriminative Pre-trained Language Models (2022.findings-acl)
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Yuan Yao, Bowen Dong, Ao Zhang, Zhengyan Zhang, Ruobing Xie, Zhiyuan Liu, Leyu Lin, Maosong Sun, Jianyong Wang
| Challenge: | Recent studies have shown promising results of prompt tuning in stimulating pre-trained language models (PLMs) for natural language processing tasks. |
| Approach: | They propose a prompt tuning framework that reformulates NLP tasks into a discriminative language modeling problem. |
| Outcome: | The proposed framework improves on text classification and question answering tasks and prevents unstable tuning problems in low-resource settings. |
PAFT: Prompt-Agnostic Fine-Tuning (2025.emnlp-main)
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| Challenge: | Prompt-agnostic fine-tuning (PAFT) improves performance by reducing overfitting to specific prompts. |
| Approach: | They propose a method that enhances robustness through dynamic prompt variation during training. |
| Outcome: | The proposed method achieves higher generalization accuracy on unseen prompts than standard methods with similar training efficiency. |
FIPO: Free-form Instruction-oriented Prompt Optimization with Preference Dataset and Modular Fine-tuning Schema (2025.coling-main)
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| Challenge: | naive prompts can enhance the task performance of large language models, but they are resource-intensive. |
| Approach: | They propose an automatic prompt optimization method that refines naive prompts according to task outputs from in-box testing models. |
| Outcome: | The proposed method is based on a large-scale dataset and performed fairly across multiple models. |
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. |
PPT: Pre-trained Prompt Tuning for Few-shot Learning (2022.acl-long)
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| Challenge: | Prompt tuning for pre-trained language models has shown remarkable performance . however, prompt tuning is still not fully explored . |
| Approach: | They propose to pre-train prompts by adding soft prompts into the pre-training stage to obtain a better initialization. |
| Outcome: | The proposed framework outperforms full-model tuning under full-data and few-shot learning settings. |