"TAPO: Task-Referenced Adaptation for Prompt Optimization"

Task-specific metric selection makes LLMs perform better at different jobs.

TAPO (Task-Referenced Adaptation for Prompt Optimization) introduces dynamic task-specific prompt optimization that adapts evaluation metrics based on task requirements, improving LLM performance across diverse applications.

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https://arxiv.org/abs/2501.06689

🤔 Original Problem:

Current automated prompt optimization methods use single metrics and lack task-specific adaptability, limiting their effectiveness across different types of tasks.

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🛠️ Solution in this Paper:

→ TAPO uses a three-module framework that dynamically selects and weights task-specific evaluation metrics.

→ The Dynamic Metric Selection module identifies task types and chooses relevant metrics like similarity, complexity, and diversity.

→ Task-Aware Prompt Evaluation combines multiple metrics into a scoring function that comprehensively assesses prompt performance.

→ Evolution-Based Optimization iteratively refines prompts through mutation and tournament selection.

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💡 Key Insights:

→ Multi-metric evaluation outperforms single-metric approaches for complex tasks

→ Task-specific metric selection significantly improves prompt quality

→ Evolution-based optimization prevents local optima stagnation

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📊 Results:

→ Achieved 80.51% accuracy on BBH dataset with GPT-4, surpassing baseline methods

→ Improved performance by 10.2% over Chain-of-Thought on math reasoning tasks

→ Demonstrated consistent performance gains across 6 diverse datasets

→ Enhanced open-source LLM performance by 6.2% compared to existing methods