About GRESEL: AI Generation Results Enriched with Simplified Explanations Based on Linguistic Features.

GRESEL: AI Generation Results Enriched with Simplified Explanations Based on Linguistic Features, is a coordinated research project funded by the Spanish Ministry of Science, Innovation and Universities. This initiative addresses the critical need to enhance the transparency and trustworthiness of responses generated by increasingly prevalent generative Artificial Intelligence (AI) applications. Recognizing the potential of these technologies across various domains, GRESEL focuses on enriching their output with simplified, linguistically grounded explanations.

The project is structured into two distinct, yet interconnected subprojects, each exploring specific facets of this overarching goal. This one is GRESEL-UAM: Financial Narratives and Literature.

It is led by Dr. Antonio Moreno-Sandoval. GRESEL-UAM delves into the intersection of financial narratives and literature. This subproject investigates how generative AI can be employed to analyze and understand complex financial texts and literary works. Furthermore, it aims to develop methods for generating AI responses in these domains that are not only accurate but also accompanied by clear and accessible explanations rooted in linguistic features present in the original texts. By focusing on these specific areas, GRESEL-UAM seeks to contribute to more nuanced and interpretable AI applications in both the financial and literary fields.

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1 to 7 of 7 Results
EN-ES financial multimodal translation model (DIMT): gemma-4-E4B LoRA adapters (EN image -> ES text) fine-tuned on annual reports from IBEX 35 companies
EN-ES financial multimodal translation model (DIMT): gemma-4-E4B LoRA adapters (EN image -> ES text) fine-tuned on annual reports from IBEX 35 companies
Jun 9, 2026
Torterolo Orta, Yanco Amor; Stanescu, Maria Alexia, 2026, "EN-ES financial multimodal translation model (DIMT): gemma-4-E4B LoRA adapters (EN image -> ES text) fine-tuned on annual reports from IBEX 35 companies", https://doi.org/10.21950/HEZRAQ, e-cienciaDatos, V1
This is a fine-tuned model for EN-ES financial multimodal translation, that is, translating directly from the English page image to the written Spanish text. This kind of multimodal translation is known as Document Image Machine Translation (DIMT). More specifically, this repository contains the low-rank adapters (LoRA) resulting from fine-tuning t...
Results and model outputs from the paper: MLLM-as-a-Judge for Financial Document Image Machine Translation
Results and model outputs from the paper: MLLM-as-a-Judge for Financial Document Image Machine Translation
Jun 9, 2026
Torterolo Orta, Yanco Amor, 2026, "Results and model outputs from the paper: MLLM-as-a-Judge for Financial Document Image Machine Translation", https://doi.org/10.21950/VBG0HP, e-cienciaDatos, V1
This dataset contains 4 CSV files, one for each model used in the experiments of the paper titled: MLLM-as-a-Judge for Financial Document Image Machine Translation. The paper explores an end-to-end (E2E) Document Image Machine Translation (DIMT) approach using Gemma 4 on financial reports from IBEX 35 companies. Since traditional Machine Translatio...
Bidirectional ES-EN financial translation model: translategemma-12b LoRA adapters using parallel annual reports from IBEX 35 companies
Bidirectional ES-EN financial translation model: translategemma-12b LoRA adapters using parallel annual reports from IBEX 35 companies
Jun 9, 2026
Torterolo Orta, Yanco Amor, 2026, "Bidirectional ES-EN financial translation model: translategemma-12b LoRA adapters using parallel annual reports from IBEX 35 companies", https://doi.org/10.21950/NKX2YN, e-cienciaDatos, V1
This is a fine-tuned model for bidirectional ES-EN financial translation. This repository contains the low-rank adapters (LoRA) resulting from fine-tuning the google/translategemma-12b-it model with a parallel dataset of financial reports from IBEX 35 companies. This model was specifically fine-tuned to be more adaptable to different input sizes (u...
The Financial Document Causality Detection Shared Task (FinCausal 2026): Dataset
Jan 30, 2026
Moreno-Sandoval, Antonio; Torterolo Orta, Yanco Amor; Stanescu, Maria Alexia; Chatzi, Melina, 2026, "The Financial Document Causality Detection Shared Task (FinCausal 2026): Dataset", https://doi.org/10.21950/H7RKHH, e-cienciaDatos, V1
The Financial Document Causality Detection Shared Task (FinCausal 2026) aims to improve causality identification in the financial domain through its texts. This shared task focuses on determining the causality associated with both events and quantified facts. For this task, a cause can be the justification of a statement or the reason explaining an...
The Financial Document Causality Detection Shared Task (FinCausal 2025): Dataset
The Financial Document Causality Detection Shared Task (FinCausal 2025): Dataset
Jul 22, 2025
Carbajo-Coronado, Blanca; Moreno-Sandoval, Antonio; Torterolo Orta, Yanco Amor; Gozalo, Paula, 2025, "The Financial Document Causality Detection Shared Task (FinCausal 2025): Dataset", https://doi.org/10.21950/V8VSSO, e-cienciaDatos, V1
The Financial Document Causality Detection Shared Task (FinCausal 2025) aims to improve causality identification in the financial domain through textual data. This shared task focuses on determining causality associated with both events and quantified facts. In this task, a cause can be the justification of a statement or the reason explaining an o...
Synthetic datasets generated by Large Language Models
Synthetic datasets generated by Large Language Models
May 27, 2025
Torterolo Orta, Yanco Amor; Roseti, Sofía Micaela; Moreno-Sandoval, Antonio, 2025, "Synthetic datasets generated by Large Language Models", https://doi.org/10.21950/YXP8Q8, e-cienciaDatos, V1
This dataset is the result of the work done in the project GRESEL-UAM: About GRESEL: AI Generation Results Enriched with Simplified Explanations Based on Linguistic Features (Resultados de Generación de IA Enriquecidos con Explicaciones Simplificadas Basadas en Características Lingüísticas). This dataset is part of the publication titled "Assessing...
Trafalgar Neo4j Database
Trafalgar Neo4j Database
May 27, 2025
Torterolo Orta, Yanco Amor; Roseti, Sofía Micaela; Moreno-Sandoval, Antonio, 2025, "Trafalgar Neo4j Database", https://doi.org/10.21950/DXGRTE, e-cienciaDatos, V1
The dataset is part of the project: GRESEL-UAM: About GRESEL: AI Generation Results Enriched with Simplified Explanations Based on Linguistic Features (Resultados de Generación de IA Enriquecidos con Explicaciones Simplificadas Basadas en Características Lingüísticas). This dataset is part of the publication titled "Assessing a Literary RAG System...
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