{"ID":2853480,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.16604","arxiv_id":"2510.16604","title":"Fine-tuning of Large Language Models for Constituency Parsing Using a Sequence to Sequence Approach","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.","short_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 synta...","url_abs":"https://arxiv.org/abs/2510.16604","url_pdf":"https://arxiv.org/pdf/2510.16604v1","authors":"[\"Francisco Jose Cortes Delgado\",\"Eduardo Martinez Gracia\",\"Rafael Valencia Garcia\"]","published":"2025-10-18T18:00:20Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}