Papers with automated prompt engineering
Learning from Contrastive Prompts: An Automated Prompt Optimization Framework (2026.findings-acl)
Copied to clipboard
| 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. |
| Outcome: | The proposed framework can generalize across open and proprietary models and NLU benchmarks. |
PRewrite: Prompt Rewriting with Reinforcement Learning (2024.acl-short)
Copied to clipboard
| 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. |
SOPL: A Sequential Optimal Learning Approach to Automated Prompt Engineering in Large Language Models (2025.findings-emnlp)
Copied to clipboard
| 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. |