This framework creates high-quality prompts by learning from past prompt performances.
A method that optimizes prompts by using optimal learning algorithms to efficiently identify effective prompt features while conserving evaluation budget.
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https://arxiv.org/abs/2501.03508
Original Problem 🎯:
→ Manual prompt engineering is time-consuming and lacks systematic guidance. The challenge intensifies when prompt evaluation is costly, like in medical research requiring expert validation.
→ Current automated approaches need numerous iterations and can't utilize correlations between similar prompts.
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Solution in this Paper 🛠️:
→ The paper introduces Sequential Optimal Learning Prompt (SOPL) framework using feature-based prompt representation.
→ It employs Bayesian regression to leverage correlations among similar prompts.
→ The system uses Knowledge-Gradient policy for efficient exploration of prompt features.
→ Mixed-integer second-order cone optimization makes the approach scalable.
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Key Insights 💡:
→ Feature-based prompts significantly broaden the search space
→ KG policy efficiently identifies high-quality prompts within limited evaluations
→ The framework outperforms benchmarks especially for challenging tasks
→ Early stopping mechanisms can reduce evaluation costs without significant performance loss
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Results 📊:
→ 6.47% improvement in average test score compared to EvoPrompt
→ 11.99% better performance than TRIPLE method
→ Achieves highest average ranking of 1.85 across 13 tasks
→ Shows lowest standard deviation of 0.0668, proving robust performance
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