Fine-tuning of Large Language Models for Constituency Parsing Using a Sequence to Sequence Approach
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866909856249675776 |
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| author | Delgado, Francisco Jose Cortes Gracia, Eduardo Martinez Garcia, Rafael Valencia |
| author_facet | Delgado, Francisco Jose Cortes Gracia, Eduardo Martinez Garcia, Rafael Valencia |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_16604 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fine-tuning of Large Language Models for Constituency Parsing Using a Sequence to Sequence Approach Delgado, Francisco Jose Cortes Gracia, Eduardo Martinez Garcia, Rafael Valencia Computation and Language 68T50 I.2.7; I.2.6 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. |
| title | Fine-tuning of Large Language Models for Constituency Parsing Using a Sequence to Sequence Approach |
| topic | Computation and Language 68T50 I.2.7; I.2.6 |
| url | https://arxiv.org/abs/2510.16604 |