Step-by-step Instructions and a Simple Tabular Output Format Improve the Dependency Parsing Accuracy of LLMs
This addresses the challenge of accurate dependency parsing for NLP practitioners, though it is incremental as it builds on existing prompting and format techniques.
The researchers tackled the problem of LLMs producing structurally invalid outputs in dependency parsing by introducing step-by-step instructions and a simplified tabular format, achieving state-of-the-art accuracy on Universal Dependencies datasets across 17 languages without hallucination or contamination.
Recent advances in large language models (LLMs) have enabled impressive performance in various tasks. However, standard prompting often struggles to produce structurally valid and accurate outputs, especially in dependency parsing. We propose a novel step-by-step instruction strategy, where universal part-of-speech tagging precedes the prediction of syntactic heads and dependency labels, and a simplified CoNLL-U like output format, our method achieves state-of-the-art accuracy on Universal Dependencies datasets across 17 languages without hallucination or contamination. We further show that multilingual fine-tuning simultaneously improves cross-language generalization performance. Our results highlight the effectiveness of explicit reasoning steps in LLM-based parsing and offer a scalable, format-consistent alternative to bracket-based approaches.