Will LLM’s make for smarter taxonomies?
How can large language models (LLMs) improve the way we work with XBRL taxonomies? In her latest blog, XBRL International Guidance Manager Revathy Ramanan explores how AI-powered tools can support taxonomy authors and software vendors, improving taxonomy quality, clarity, and usability.
Recent experiments show that LLMs can streamline taxonomy development by identifying redundant phrases in labels, ensuring consistency, and enhancing clarity—helping taxonomy authors refine their work more efficiently. They can also provide real-time quality checks, such as verifying presentation tree structures, ensuring best practices are followed. Another key use case? Making Formula rules easier to interpret. LLMs can break down formulas into business-friendly explanations, helping both technical and non-technical users understand the logic behind validation rules.
Beyond these refinements, Revathy also considers a more transformative application: using LLMs to rethink taxonomy navigation. Traditionally, taxonomies rely on hierarchical structures, which can be cumbersome to explore. By leveraging AI, users can query taxonomies in natural language, gaining context-rich summaries of disclosures and identifying relevant concepts more intuitively.
Could LLMs reshape the way taxonomies are designed and explored? Revathy’s insights suggest a future where AI-driven assistance makes taxonomy interactions smarter, faster, and more accessible.
Read the full blog here to learn more about the exciting potential of AI-driven taxonomy tools.
