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This paper presents a document-based question-answering (DBQA) system utilizing a large language model (LLM) to assist students in retrieving relevant information from official university documents. The system effectively processes machine-readable documents by leveraging pre-trained multilingual embeddings and a transformer-based QA model while supporting both Serbian and English queries. The experimental setup focused on extracting relevant context using cosine similarity and answering queries using a fine-tuned QA pipeline, achieving an average accuracy of 84.16%. Evaluation results highlight the system’s strengths in retrieving factual answers while identifying challenges related to answer format variations and partial extractions. This research demonstrates the potential of LLM-based DBQA systems in academic and institutional environments, improving information accessibility for students.

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From Documents to Answers: A University-Focused DBQA System with LLMs

  • 会议论文

  • 发表日期：

    2026年

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  • Matija Dodović,
  • Janko Tufegdžić,
  • Luka Hrvačević,
  • Dražen Drašković

摘要

This paper presents a document-based question-answering (DBQA) system utilizing a large language model (LLM) to assist students in retrieving relevant information from official university documents. The system effectively processes machine-readable documents by leveraging pre-trained multilingual embeddings and a transformer-based QA model while supporting both Serbian and English queries. The experimental setup focused on extracting relevant context using cosine similarity and answering queries using a fine-tuned QA pipeline, achieving an average accuracy of 84.16%. Evaluation results highlight the system’s strengths in retrieving factual answers while identifying challenges related to answer format variations and partial extractions. This research demonstrates the potential of LLM-based DBQA systems in academic and institutional environments, improving information accessibility for students.