StraGo: Harnessing Strategic Guidance for Prompt Optimization
Open Access
- 1 January 2024
- conference paper
- Published by Association for Computational Linguistics (ACL)
- p. 10043-10061
Abstract
Yurong Wu, Yan Gao, Bin Benjamin Zhu, Zineng Zhou, Xiaodi Sun, Sheng Yang, Jian-Guang Lou, Zhiming Ding, Linjun Yang. Findings of the Association for Computational Linguistics: EMNLP 2024. 2024.Keywords
All Related Versions
This publication has 8 references indexed in Scilit:
- Unleashing the Potential of Large Language Models as Prompt Optimizers: Analogical Analysis with Gradient-based Model OptimizersProceedings of the AAAI Conference on Artificial Intelligence, 2025
- Large Language Models can Accurately Predict Searcher Preferences2024
- Prompt Engineering a Prompt Engineer2024
- Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language ProcessingACM Computing Surveys, 2023
- Automatic Prompt Optimization with “Gradient Descent” and Beam Search2023
- What Disease Does This Patient Have? A Large-Scale Open Domain Question Answering Dataset from Medical ExamsApplied Sciences, 2021
- The Power of Scale for Parameter-Efficient Prompt Tuning2021
- AutoPrompt: Eliciting Knowledge from Language Models with Automatically Generated Prompts2020