Reproduce “AgentODRL: A Large Language Model-based Multi-agent System for ODRL Generation” on OpenResearch
Turn your favorite coding agents into research agents that review literature, develop hypotheses, and run experiments.
Universal app for Apple silicon and Intel. Requires macOS 11 or later.
About this paper
Wanle Zhong, Keman Huang, Xiaoyong Du
AgentODRL, a multi-agent system leveraging Large Language Models, automates the generation of complex Open Digital Rights Language (ODRL) policies from natural language descriptions. This system consistently achieved grammar scores exceeding 99% and improved average semantic scores by 14.52% over prior monolithic LLM approaches while efficiently handling parallel and recursive policy structures.
Key insights
- Decomposing the complex NL-to-ODRL conversion into distinct sub-tasks handled by specialized agents significantly improves both syntactic correctness and semantic fidelity.
- An iterative "generate-validate-reflect" closed-loop mechanism, utilizing SHACL constraints, effectively mitigates LLM syntactic hallucinations and ensures high ODRL grammar compliance.
- An adaptive Orchestrator agent, which intelligently classifies input complexity and routes tasks, optimizes computational resource usage while maintaining near-optimal performance.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.