Flatness-Aware Prompt Selection Improves Accuracy and Sample Efficiency (2023.findings-emnlp)
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| Challenge: | Manually "engineering" prompts for large language models can be laborious and time-intensive. |
| Approach: | They propose a new metric to quantify the expected utility of a language prompt. |
| Outcome: | The proposed metric outperforms previous prompt selection metrics with 10% increase in Pearson correlation across 6 classification benchmarks and the prompt selected by the proposed meter gains 5% higher accuracy than previous metrics. |
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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. |
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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PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer (2023.emnlp-main)
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| Challenge: | Existing prompt tuning methods have training instability issues due to large variance of scores . existing prompt tuning algorithms have training stability issues due a slight change of input data . |
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Learning from Contrastive Prompts: An Automated Prompt Optimization Framework (2026.findings-acl)
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| Challenge: | Existing prompt optimization methods often underperform due to learning exclusively from incorrect samples. |
| Approach: | They propose a framework that leverages contrastive prompts to distinguish between high- and low-performing cases. |
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Demystifying Prompts in Language Models via Perplexity Estimation (2023.findings-emnlp)
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| Challenge: | Language models can be prompted to perform a wide variety of tasks with zero- and few-shot learning. |
| Approach: | They propose a method to automatically extend a small seed set of manually written prompts by paraphrasing with GPT3 and backtranslation. |
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PMPO: Probabilistic Metric Prompt Optimization for Small and Large Language Models (2025.findings-emnlp)
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| Challenge: | Existing methods evaluate candidate prompts by sampling full outputs, often coupled with self critique or human annotated preferences, which limits scalability, especially for smaller models or models that are not instruction tuned. |
| Approach: | They propose a framework that uses token level cross entropy as a direct, lightweight evaluation signal to evaluate candidate prompts. |
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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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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. |
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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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| Outcome: | The proposed model reduces the variance of the generated code by 40% . the proposed model is based on a large-scale dataset of 132,000 prompt variants . |
Exploiting Language Model Prompts Using Similarity Measures: A Case Study on the Word-in-Context Task (2022.acl-short)
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| Challenge: | Existing few-shot approaches fail on the semantic distinction task of the Word-in-Context dataset. |
| Approach: | They propose a prompt-based approach which boosts few-shot performance to the level of fully supervised methods by using similarity metrics. |
| Outcome: | The proposed technique boosts few-shot performance to the level of fully supervised methods. |