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

• URL: http://arxiv.org/abs/2510.16604v1
• Date: Sat, 18 Oct 2025 18:00:20 GMT
• Title: Fine-tuning of Large Language Models for Constituency Parsing Using a Sequence to Sequence Approach
• Authors: Francisco Jose Cortes Delgado, Eduardo Martinez Gracia, Rafael Valencia Garcia,
• Abstract summary: This work explores a novel approach to phrase-structure analysis by fine-tuning large language models.<n>The main objective is to extend the capabilities of MiSintaxis, a tool designed for teaching Spanish syntax.<n>The results demonstrate high accuracy in phrase-structure analysis and highlight the potential of this methodology.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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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