CoPrompter helps prompt engineers systematically fix misalignments between LLM outputs and user requirements
A framework that breaks down complex prompts into atomic instructions for better LLM alignment, to evaluate if LLMs actually follow all your prompt instructions.
https://arxiv.org/abs/2411.06099
🎯 Original Problem:
Prompt engineers face significant challenges in aligning LLM outputs with complex prompts containing 5+ instructions. The current process requires 10+ iterations of manual inspection, making it time-consuming and inefficient.
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🔧 Solution in this Paper:
→ CoPrompter breaks down complex prompts into atomic instructions and converts them into evaluation criteria questions
→ It generates multiple LLM responses and evaluates them against these criteria to produce alignment scores
→ The system provides detailed reports showing where and how often misalignments occur
→ Users can iteratively refine prompts based on systematic feedback rather than trial-and-error
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💡 Key Insights:
→ Complex prompts with multiple instructions often face misalignment due to instruction overlooking and misinterpretation
→ Breaking down instructions into atomic units helps in systematic evaluation
→ User-in-loop control over evaluation criteria is crucial for evolving requirements
→ Systematic evaluation reports help prioritize which parts of prompts need refinement
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📊 Results:
→ Improved ability to identify and refine instruction alignment compared to traditional methods
→ High System Usability Scale scores indicating good integration into workflows
→ Successfully helped clarify user requirements and provided greater control over response evaluation
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