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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Aug 31·5 min read
SEPO: Evidence-Grounded Prompt Optimization via Structural Editing
TL;DR
SEPO (Structural, Evidence-grounded Prompt Optimization) introduces a novel approach to prompt optimization by enabling localized edits in a two-layer prompt schema. This method significantly improves performance, achieving 61.9% and 73.3% macro accuracy on Llama-3.1-8B-Instruct and Qwen3-8B, respectively.
✦ Why It Matters
Implement SEPO for more effective and interpretable prompt optimization in your AI applications today.
Key Takeaways
- Utilize SEPO for localized prompt edits to enhance interpretability.
- Leverage edit-effect lineage feedback to track optimization impacts.
- Achieve higher accuracy with fewer optimization tokens using SEPO.
- Consider shorter prompts for improved efficiency in AI tasks.
How It Works
SEPO operates by allowing localized edits to specific, stable units within a two-layer prompt schema. This structure enables the optimizer to maintain a record of how each edit affects the performance of the prompt, linking changes to specific examples that are improved or degraded.
By carrying this edit-effect lineage forward, SEPO can make more informed decisions in subsequent optimization iterations.