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Πτυχιακές εργασίες

Μόνιμο URI για αυτήν τη συλλογήhttps://pyxida.aueb.gr/handle/123456789/11719

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Πλοήγηση Πτυχιακές εργασίες ανά Θέμα "Large Language Models (LLMs)"

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  • Μικρογραφία εικόνας
    Τεκμήριο
    Retrieval augmented generation on regulatory documents
    (2025-06-20) Chasandras, Ioannis; Χασάνδρας, Ιωάννης; Androutsopoulos, Ion; Chlapanis, Odysseas
    This thesis investigates the application of Retrieval Augmented Generation (RAG) in regulatory procedures through the emerging field of Regulatory NLP. Based on real-world regulatory documents, the study evaluates the performance of commercial retrieval models and introduces advanced, hybrid retrieval techniques tailored for legal compliance tasks. Given the critical need for precision and completeness in the legal domain, new algorithms that utilize Large Language Models (LLMs) are developed to enhance regulatory question-answering. The work also includes an adversarial evaluation of RePASs, a metric focused on legal obligations. Through participation in the RIRAG-2025 shared task, the thesis demonstrates both the promise and current limitations of AI systems in regulatory settings, emphasizing the need for further exploration in this field.
  • Μικρογραφία εικόνας
    Τεκμήριο
    Leveraging retrieval-augmented generation for student support: a document-centric QA system for the AUEB informatics studies guide
    (2025-07) Mitsakis, Nikos; Μητσάκης, Νικόλαος; Androutsopoulos, Ion; Stafylakis, Themos
    This thesis examines the design, development, and evaluation of a Retrieval-Augmented Generation (RAG) system specifically designed to support undergraduate students in the Department of Informatics at the Athens University of Economics and Business (AUEB). The central objective is to create a cost-effective yet high-quality AI assistant capable of answering studies guide-related questions, ensuring that all responses are explicitly grounded in the latest edition of the department's official Studies Guide. To achieve this, the system ingests the newest version of the Studies Guide. It represents its contents at three levels of granularity: chunks (bodies of text corresponding to paragraphs or groups of paragraphs on a specific topic, based on the document’s structure), sentences (extracted by sentence tokenizing each chunk), and propositions (decontextualized factual statements synthetically generated from the chunks). The retrieval architecture explores traditional lexical search (BM25), dense vector search, and a hybrid ensemble retriever to maximize retrieval coverage and relevance. Question-answering capabilities are assessed using both real-world and synthetic QA pairs, with the generation module leveraging self-hosted state-of-the-art large language models (LLMs). The thesis conducts a comprehensive evaluation across all document granularities and retrieval configurations, employing both classical information retrieval metrics and more modern LLM-based evaluation. Results demonstrate the feasibility of delivering a factual, responsive, and modular assistant using modest computational resources. The thesis further discusses the limitations and potential extensions of the approach, aiming to provide a blueprint for deploying similar RAG-based assistants in other academic settings.