GPTree: Improving explainability of AI models via decision trees

The fusion of large language models (LLMs) with traditional decision trees represents a significant advancement in making artificial intelligence both powerful and interpretable for complex decision-making tasks.

Key Innovation; GPTree combines the explainability of decision trees with the advanced reasoning capabilities of large language models to create a more effective and transparent decision-making system.

• The framework eliminates the need for feature engineering and prompt chaining, requiring only a task-specific prompt to function
• GPTree utilizes a tree-based structure to dynamically split samples, making the decision process more efficient and traceable
• The system incorporates an expert-in-the-loop feedback mechanism that allows human experts to refine and rebuild decision paths

Performance Metrics; In a practical application focused on identifying potential “unicorn” startups at their inception stage, GPTree demonstrated remarkable results.

• The system achieved a 7.8% precision rate in identifying future unicorn startups
• This performance significantly outpaced both GPT-4 with few-shot learning and human expert decision-makers, who achieved between 3.1% and 5.6% precision rates
• The results validate GPTree’s effectiveness in handling complex, real-world decision-making scenarios

Technical Architecture; The framework addresses key limitations of both traditional decision trees and neural networks.

• Traditional decision trees, while explainable, struggle with non-linear and high-dimensional data
• Neural networks excel at pattern recognition but lack transparency in their decision-making process
• GPTree bridges this gap by maintaining explainability while handling complex data patterns effectively

Human-AI Collaboration; The integration of human expertise plays a crucial role in GPTree’s functionality.

• The expert-in-the-loop feedback mechanism enables continuous improvement of the system
• Human experts can intervene to refine decision paths based on their domain knowledge
• This collaborative approach emphasizes the importance of maintaining human oversight in AI-driven decision-making

Future Implications; The development of GPTree represents a significant step toward more transparent and effective AI-powered decision-making systems, though questions remain about its scalability across different domains and the optimal balance between automation and human intervention.