Category: Research
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The Role of Clarifying Questions in Conversational Recommender Systems: Balancing Explicitness
Author: Anna Baghumyan Supervisor: Julia Neidhardt, Co-Supervisor: Ahmadou Wagne
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Evaluating Agentic Retrieval Augmented Generation in Open Tender Evaluations
Author: Franz Ottitsch Supervisor: Julia Neidhardt, Co-Supervisor: Thomas E. Kolb Abstract Public tender evaluation requires contracting authorities to assess bidder responses against defined award criteria across long, semi structured documents, a process that remains largely manual, time consuming and costly. Retrieval augmented generation (RAG) systems, particularly agentic variants with iterative retrieval and task decomposition, offer…
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Examining Tech Bias – Analyzing Career Recommendations in LLMs through Demographic Persona-Based Prompting
Supervisor: Julia Neidhardt
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The impact of using Large Language Models on the performance of Recommender Systems
Author: Michael Schmiedmayer Supervisor: Julia Neidhardt, Co-Supervisor: Ahmadou Wagne Abstract This thesis investigates the integration of Large Language Models (LLMs) into Conversational Recommender Systems (CRSs), evaluating the impact of model scale and hyperparameter configurations on retrieval performance. Moving beyond purely synthetic benchmarks, this research introduces a novel, end-to-end evaluation framework grounded in real-world user behaviour.…
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Data-Centric AI for Conceptual Modeling: Cleansed Data and GNN-LLM based Recommender for UML Class Diagrams
Author: Andjela Djelic Supervisor: Dominik Bork, Co-Supervisor: Julia Neidhardt Abstract In the Model-Driven Engineering (MDE) domain, Machine Learning (ML)-based recommender systems can assist developers during conceptual modeling by suggesting plausible modeling elements based on the current state of a model. However, the conceptual model datasets commonly used for such machine learning research, often mined from…