LLMs can learn grammar adaptation patterns from examples and apply them to new versions, achieving 100% consistency on medium-sized grammars but failing on large-scale ones—suggesting LLMs work best for targeted, smaller grammar updates.
This paper shows how Large Language Models can automatically adapt domain-specific language grammars when their underlying models change, reducing manual work. Testing on real-world languages shows LLMs work well for complex scenarios but struggle with very large grammars (300+ rules).