LLM Dependency Parsing with In-Context Rules

AbstractWe study whether incorporating rules (in various formats) can aid large language models to perform dependency parsing. We consider a paradigm in which LLMs first produce symbolic rules given fully labeled examples, and the rules are then provided in a subsequent call that performs the actual parsing. In addition, we experiment with providing human-created annotation guidelines in-context to the LLMs. We test on eight low-resource languages from Universal Dependencies, finding that while both methods for rule incorporation improve zero-shot performance, the benefit disappears with a few labeled in-context examples.