MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization
Fuente:
arXiv
Gespeichert in:
| Hauptverfasser: | , , , , , , , , |
|---|---|
| Format: | Preprint |
| Veröffentlicht: |
2024
|
| Schlagworte: | |
| Online-Zugang: | |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| _version_ | 1866913418029563904 |
|---|---|
| author | Chen, Yuyan Wen, Zhihao Fan, Ge Chen, Zhengyu Wu, Wei Liu, Dayiheng Li, Zhixu Liu, Bang Xiao, Yanghua |
| author_facet | Chen, Yuyan Wen, Zhihao Fan, Ge Chen, Zhengyu Wu, Wei Liu, Dayiheng Li, Zhixu Liu, Bang Xiao, Yanghua |
| contents | Prompt engineering, as an efficient and effective way to leverage Large Language Models (LLM), has drawn a lot of attention from the research community. The existing research primarily emphasizes the importance of adapting prompts to specific tasks, rather than specific LLMs. However, a good prompt is not solely defined by its wording, but also binds to the nature of the LLM in question. In this work, we first quantitatively demonstrate that different prompts should be adapted to different LLMs to enhance their capabilities across various downstream tasks in NLP. Then we novelly propose a model-adaptive prompt optimizer (MAPO) method that optimizes the original prompts for each specific LLM in downstream tasks. Extensive experiments indicate that the proposed method can effectively refine prompts for an LLM, leading to significant improvements over various downstream tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04118 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization Chen, Yuyan Wen, Zhihao Fan, Ge Chen, Zhengyu Wu, Wei Liu, Dayiheng Li, Zhixu Liu, Bang Xiao, Yanghua Computation and Language Artificial Intelligence Prompt engineering, as an efficient and effective way to leverage Large Language Models (LLM), has drawn a lot of attention from the research community. The existing research primarily emphasizes the importance of adapting prompts to specific tasks, rather than specific LLMs. However, a good prompt is not solely defined by its wording, but also binds to the nature of the LLM in question. In this work, we first quantitatively demonstrate that different prompts should be adapted to different LLMs to enhance their capabilities across various downstream tasks in NLP. Then we novelly propose a model-adaptive prompt optimizer (MAPO) method that optimizes the original prompts for each specific LLM in downstream tasks. Extensive experiments indicate that the proposed method can effectively refine prompts for an LLM, leading to significant improvements over various downstream tasks. |
| title | MAPO: Boosting Large Language Model Performance with Model-Adaptive Prompt Optimization |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2407.04118 |