PRewrite: Prompt Rewriting with Reinforcement Learning

PRewrite: Prompt Rewriting with Reinforcement Learning

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.
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