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arXiv · 2510.16604

Fine-tuning of Large Language Models for Constituency Parsing Using a Sequence to Sequence Approach

Francisco Jose Cortes Delgado·Eduardo Martinez Gracia·Rafael Valencia Garcia
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Abstract

Recent advances in natural language processing with large neural models have opened new possibilities for syntactic analysis based on machine learning. This work explores a novel approach to phrase-structure analysis by fine-tuning large language models (LLMs) to translate an input sentence into its corresponding syntactic structure. The main objective is to extend the capabilities of MiSintaxis, a tool designed for teaching Spanish syntax. Several models from the Hugging Face repository were fine-tuned using training data generated from the AnCora-ES corpus, and their performance was evaluated using the F1 score. The results demonstrate high accuracy in phrase-structure analysis and highlight the potential of this methodology.

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Francisco Jose Cortes Delgado, Eduardo Martinez Gracia, Rafael Valencia Garcia. 2025-10-18. Fine-tuning of Large Language Models for Constituency Parsing Using a Sequence to Sequence Approach. https://arxiv.org/abs/2510.16604

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