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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2510.02653 |
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| _version_ | 1866916987164164096 |
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| author | Pozo, Micaela Fuel Saltos, Andrea Guatumillo Llumiquinga, Yeseña Tipan Aguirre, Kelly Lascano Jara, Marilyn Castillo Mejia-Escobar, Christian |
| author_facet | Pozo, Micaela Fuel Saltos, Andrea Guatumillo Llumiquinga, Yeseña Tipan Aguirre, Kelly Lascano Jara, Marilyn Castillo Mejia-Escobar, Christian |
| contents | This study presents the development of Geolog-IA, a novel conversational system based on artificial intelligence that responds naturally to questions about geology theses from the Central University of Ecuador. Our proposal uses the Llama 3.1 and Gemini 2.5 language models, which are complemented by a Retrieval Augmented Generation (RAG) architecture and an SQLite database. This strategy allows us to overcome problems such as hallucinations and outdated knowledge. The evaluation of Geolog-IA's performance with the BLEU metric reaches an average of 0.87, indicating high consistency and accuracy in the responses generated. The system offers an intuitive, web-based interface that facilitates interaction and information retrieval for directors, teachers, students, and administrative staff at the institution. This tool can be a key support in education, training, and research and establishes a basis for future applications in other disciplines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_02653 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Geolog-IA: Conversational System for Academic Theses Pozo, Micaela Fuel Saltos, Andrea Guatumillo Llumiquinga, Yeseña Tipan Aguirre, Kelly Lascano Jara, Marilyn Castillo Mejia-Escobar, Christian Artificial Intelligence Information Retrieval This study presents the development of Geolog-IA, a novel conversational system based on artificial intelligence that responds naturally to questions about geology theses from the Central University of Ecuador. Our proposal uses the Llama 3.1 and Gemini 2.5 language models, which are complemented by a Retrieval Augmented Generation (RAG) architecture and an SQLite database. This strategy allows us to overcome problems such as hallucinations and outdated knowledge. The evaluation of Geolog-IA's performance with the BLEU metric reaches an average of 0.87, indicating high consistency and accuracy in the responses generated. The system offers an intuitive, web-based interface that facilitates interaction and information retrieval for directors, teachers, students, and administrative staff at the institution. This tool can be a key support in education, training, and research and establishes a basis for future applications in other disciplines. |
| title | Geolog-IA: Conversational System for Academic Theses |
| topic | Artificial Intelligence Information Retrieval |
| url | https://arxiv.org/abs/2510.02653 |