Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·27d ago·5 min read
Failure-Guided Co-Evolution of Prompts and Training Data
TL;DR
FORGE is a novel framework that co-evolves prompts and training data by leveraging failure signals, resulting in a 16.52 percentage point improvement over traditional methods. This approach not only enhances prompt optimization but also synthesizes new training data effectively.
✦ Why It Matters
Implement FORGE to enhance your language model's performance through dynamic prompt and data co-evolution.
Key Takeaways
- Utilize FORGE to improve prompt optimization in your projects.
- Explore failure signals to guide data synthesis effectively.
- Apply mutation strategies for generating new training data.
- Leverage synthesized data for better model performance in diverse tasks.
How It Works
FORGE operates by identifying failure modes from model executions, which are then used to inform both prompt adjustments and the generation of new training data. It employs four mutation strategies to create diverse training examples that address the identified weaknesses, allowing the model to learn from a broader range of scenarios.