README.md

## Introduction

LLMTrad-IBE is a strategic research initiative dedicated to overcoming the digital divide affecting the minority Romance languages of the Iberian Peninsula. By leveraging state-of-the-art Natural Language Processing (NLP), we aim to ensure these languages are not left behind in the era of Artificial Intelligence.

This project is a key component of the AI-TraLow coordinated framework (AI-Driven Translation for Low-Resource Languages and Cultures), supported by the Spanish Ministry of Science, Innovation, and Universities (MCIU/AEI/10.13039/501100011033/FEDER, UE) under reference PID2024-158157OB-C33.

## Mission and Scope

Our research focuses on the development, adaptation, and evaluation of Large Language Models (LLMs) for four specific linguistic varieties characterized by limited digital resources:

* Asturian
* Aragonese
* Aranese
* Eonavian

## Strategic Research Areas

We employ a hybrid methodology that integrates the structural precision of symbolic systems with the generative power of neural architectures:

* LLM Specialization: Fine-tuning decoder-only architectures and exploring parameter-efficient strategies (PEFT) for translation.
* Knowledge Distillation: Developing compact and efficient models to facilitate sustainable deployment in standard computing environments.
* Resource Synthesis: Expanding Apertium-based lexical resources and curating high-quality benchmarks, including FLORES+ and NTREX adaptations.
* Ethical AI: Implementing rigorous evaluation frameworks to detect and mitigate gender bias and ensure linguistic authenticity.

## Collaborative Network

LLMTrad-IBE thrives on the synergy between leading academic institutions:

* Universitat Oberta de Catalunya (UOC) — Coordinating Institution
* Universitat Autònoma de Barcelona (UAB)
* Universidad de Oviedo
* Universidad de Zaragoza

## Commitment to Open Science

As part of our commitment to the scientific community and linguistic heritage, all models, datasets, and tools developed within this project are released under permissive open-source licenses.