Agentic data science with the Data Agent Kit
cloud.google.com·6h ago
AI Research
arXiv CS.AI·Aug 7·5 min read
Learning Context-Free Grammars for Grammar-Constrained Decoding via Declarative Agentic Programming with Guarantees
TL;DR
Autogrammar is an innovative agent that learns context-free grammars from documentation and execution data, significantly improving language model performance in generating syntactically valid programs. It achieves near-perfect precision and reduces execution time by 3.8x while maintaining accuracy.
✦ Why It Matters
Implement Autogrammar to enhance your language model's ability to generate valid DSL programs today.
Key Takeaways
- Utilize Autogrammar for generating context-free grammars from DSL documentation.
- Leverage execution data to improve grammar precision significantly.
- Apply temporal logic constraints to optimize execution time.
- Expect improved LM performance on real-world tasks with Autogrammar.
How It Works
Autogrammar learns context-free grammars by analyzing both documentation and execution data. It is structured as a Kripke model, where its nondeterministic choices are influenced by a language model.
This allows for the application of linear temporal logic constraints, which guide the agent's behavior and enhance the generation of syntactically valid programs.