Scaling and Adapting Large Language Models for Portuguese Open Information Extraction: A Comparative Study of Fine-Tuning and LoRA

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3A4232JKTX" target="_blank" >RIV/00216208:11320/26:4232JKTX - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1007/978-3-031-79035-5_30" target="_blank" >http://dx.doi.org/10.1007/978-3-031-79035-5_30</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-79035-5_30" target="_blank" >10.1007/978-3-031-79035-5_30</a>

Result language

angličtina

Original language name

Scaling and Adapting Large Language Models for Portuguese Open Information Extraction: A Comparative Study of Fine-Tuning and LoRA

Original language description

This paper comprehensively investigates the efficacy of different adaptation techniques for Large Language Models (LLMs) in the context of Open Information Extraction (OpenIE) for Portuguese. We compare Full Fine-Tuning (FFT) and Low-Rank Adaptation (LoRA) across a model with 0.5B parameters. Our study evaluates the impact of model size and adaptation method on OpenIE performance, considering precision, recall, and F1 scores, as well as computational efficiency during training and inference phases. We contribute to a high-performing LLM and novel insights into the trade-offs between model scale, adaptation technique, and cross-lingual transferability in the OpenIE task. Our findings reveal significant performance variations across different configurations, with LoRA demonstrating competitive results. We also analyze the linguistic nuances in the Portuguese OpenIE that pose challenges for models primarily trained on English data. This research advances our understanding of LLM adaptation for specialized NLP tasks and provides practical guidelines for deploying these models in resource-constrained and multilingual scenarios. Our work has implications for the broader cross-lingual open information extraction field and contributes to the ongoing discourse on efficient fine-tuning strategies for large pre-trained models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

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Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Lect. Notes Comput. Sci.

ISBN

978-3-031-79034-8

ISSN

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e-ISSN

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Number of pages

15

Pages from-to

427-441

Publisher name

Springer Science and Business Media Deutschland GmbH

Place of publication

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Event location

Belém do Pará

Event date

Jan 1, 2026

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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