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Geolog-IA: Conversational System for Academic Theses

arXiv 2510.02653 ↗First posted 3 Oct 2025cs.AI cs.IRcited by 0

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Abstract (arXiv)

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 This strategy allows us to overcome problems such as hallucinations and outdated knowledge.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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 10 of its 33 references that are in the map. First posted on arXiv 3 Oct 2025.

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References · 10

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  3. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  4. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  5. 2016How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generationcited by 356
  6. 2019Gradio: Hassle-Free Sharing and Testing of ML Models in the Wildcited by 176
  7. 2024Data-efficient Fine-tuning for LLM-based Recommendationcited by 103
  8. 2023GeoGalactica: A Scientific Large Language Model in Geosciencecited by 32
  9. 2024RAG based Question-Answering for Contextual Response Prediction Systemcited by 14
  10. 2024Unimib Assistant: designing a student-friendly RAG-based chatbot for all their needscited by 4

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