Agentic data science with the Data Agent Kit
cloud.google.com·8h ago
AI Research
arXiv CS.AI·7d ago·5 min read
Law And Order: Tax Law Autoformalization
TL;DR
Law&Order is a neuro-symbolic framework that automates the formalization of tax law into executable symbolic programs, achieving 100% accuracy on held-out tax returns. This approach combines large language model synthesis with rigorous verification methods, outperforming traditional models significantly.
✦ Why It Matters
Consider integrating neuro-symbolic frameworks like Law&Order to enhance the accuracy of legal software solutions.
Key Takeaways
- Explore neuro-symbolic frameworks for legal text processing.
- Utilize large language models for synthesis in legal applications.
- Implement rigorous verification methods to ensure accuracy.
- Evaluate performance against independent benchmarks for reliability.
How It Works
Law&Order formalizes tax law by creating a neuro-symbolic framework that aligns legal components with symbolic representations. It establishes structural correspondence, matching elements like cells and schedules, and denotational correspondence, ensuring that symbolic components perform the computations dictated by their legal counterparts.
The framework combines LLM synthesis with verification techniques, allowing for iterative error correction based on real-world tax returns.