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Arxiv
1d
209
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Bandit-Based Prompt Design Strategy Selection Improves Prompt Optimizers
- Prompt optimization aims to enhance the performance of large language models through the discovery of effective prompts.
- The Autonomous Prompt Engineering Toolbox (APET) has integrated various prompt design strategies into the optimization process.
- A new method called Optimizing Prompts with sTrategy Selection (OPTS) introduces explicit selection mechanisms for prompt design, including a Thompson sampling-based approach.
- Experiments optimizing prompts for Llama-3-8B-Instruct and GPT-4o mini LLMs show that the selection of prompt design strategies improves performance, with the Thompson sampling-based mechanism yielding the best results.
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