Papers with automatic prompt engineering methods
Local Prompt Optimization (2025.naacl-short)
Copied to clipboard
| Challenge: | Existing prompt optimization methods optimize prompts globally, but they lack the correct words for a task. |
| Approach: | They propose a local prompt optimization process that integrates with any general automatic prompt engineering method to optimize a prompt over a large vocabulary. |
| Outcome: | The proposed method improves on Math Reasoning and BIG-bench Hard benchmarks and shows that it can converge to the optimal prompt faster than global methods. |
Meta-Learning of Prompt Generation for Lightweight Prompt Engineering on Language-Model-as-a-Service (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Language-Model-as-a-Services (LMaaSs) support a variety of user tasks through in-context learning from prompts. |
| Approach: | They propose a lightweight automatic prompt generation method that meta-trains a prompt generation model to enable robust learning from the contexts created by the generated prompts. |
| Outcome: | The proposed method improves performance on unseen tasks by 19.4% compared to the state-of-the-art prompt generation method. |