10 Jun 2024 | Weize Kong, Spurthi Amba Hombaiah, Mingyang Zhang, Qiaozhu Mei, Michael Bendersky
**Prompt Engineering and Automation:**
Prompt engineering is crucial for developing LLM-based applications, but it is often done manually and can be time-consuming and sub-optimal. The paper addresses these issues by proposing PRewrite, an automated method to rewrite under-optimized prompts into more effective ones using reinforcement learning (RL).
**PRewrite Overview:**
- **Objective:** Optimize prompts via rewriting using RL.
- **Method:** Train a rewriter LLM (e.g., PaLM 2-S) to generate a rewritten prompt from an initial prompt.
- **Process:** The rewriter LLM is instructed to generate a new prompt using a meta prompt, which is then used by the task LLM to generate the final output. Rewards are computed based on the task output and used to fine-tune the rewriter LLM using RL.
**Contributions:**
- Proposes PRewrite, a novel automated prompt engineering approach.
- Develops two rewriting strategies: inference (PRewrite-I) and search (PRewrite-S).
- Conducts experiments on diverse benchmark datasets, demonstrating PRewrite's effectiveness and state-of-the-art performance.
**Experiments and Analysis:**
- **Setup:** Evaluates PRewrite on datasets like AG News, SST-2, Natural Questions (NQ), and GSM8K.
- **Results:** PRewrite consistently improves over initial prompts, with larger improvements in datasets with more room for optimization. PRewrite-S outperforms PRewrite-I and baseline models.
- **Case Studies:** PRewrite produces interpretable and creative prompts, such as adding in-context examples and chain-of-thought prompts.
**Related Work:**
- Discusses previous works on automated prompt engineering, including gradient-based search, RL-based methods, and blackbox LLMs like PaLM 2 and GPT models.
**Limitations:**
- Limited initial and meta prompts tested on four datasets.
- Future work could explore more combinations and datasets to enhance generality.
**Conclusion:**
PRewrite effectively optimizes prompts using RL, demonstrating its potential for improving LLM performance in various tasks.**Prompt Engineering and Automation:**
Prompt engineering is crucial for developing LLM-based applications, but it is often done manually and can be time-consuming and sub-optimal. The paper addresses these issues by proposing PRewrite, an automated method to rewrite under-optimized prompts into more effective ones using reinforcement learning (RL).
**PRewrite Overview:**
- **Objective:** Optimize prompts via rewriting using RL.
- **Method:** Train a rewriter LLM (e.g., PaLM 2-S) to generate a rewritten prompt from an initial prompt.
- **Process:** The rewriter LLM is instructed to generate a new prompt using a meta prompt, which is then used by the task LLM to generate the final output. Rewards are computed based on the task output and used to fine-tune the rewriter LLM using RL.
**Contributions:**
- Proposes PRewrite, a novel automated prompt engineering approach.
- Develops two rewriting strategies: inference (PRewrite-I) and search (PRewrite-S).
- Conducts experiments on diverse benchmark datasets, demonstrating PRewrite's effectiveness and state-of-the-art performance.
**Experiments and Analysis:**
- **Setup:** Evaluates PRewrite on datasets like AG News, SST-2, Natural Questions (NQ), and GSM8K.
- **Results:** PRewrite consistently improves over initial prompts, with larger improvements in datasets with more room for optimization. PRewrite-S outperforms PRewrite-I and baseline models.
- **Case Studies:** PRewrite produces interpretable and creative prompts, such as adding in-context examples and chain-of-thought prompts.
**Related Work:**
- Discusses previous works on automated prompt engineering, including gradient-based search, RL-based methods, and blackbox LLMs like PaLM 2 and GPT models.
**Limitations:**
- Limited initial and meta prompts tested on four datasets.
- Future work could explore more combinations and datasets to enhance generality.
**Conclusion:**
PRewrite effectively optimizes prompts using RL, demonstrating its potential for improving LLM performance in various tasks.