Guest Editors
Assoc. Prof. Marina Bagić Babac
Email: marina.bagic@fer.hr
Affiliation: Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, 10000, Croatia
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Research Interests: Natural Language Processing, Large Language Models, Machine Learning, Deep Learning
Summary
Large Language Models (LLMs) have become a core technology in modern artificial intelligence, enabling advanced natural language understanding, generation, and reasoning across a wide range of applications. However, standalone LLMs often face limitations related to outdated knowledge, hallucinations, and limited domain adaptability. Knowledge-enhanced approaches, particularly retrieval-augmented generation (RAG), have emerged as effective solutions by grounding model outputs in external, up-to-date, and contextually relevant information.
The aim of this Special Issue is to provide a broad and inclusive platform for recent research on the development, evaluation, and application of large language models, with an emphasis on reliability, robustness, and knowledge integration. The scope covers foundational studies on LLM architectures, evaluation methodologies, and benchmarking practices, as well as empirical investigations into factual consistency, contextual understanding, and real-world performance.
Relevant topics include, but are not limited to:
· Architectures and training of large language models
· Evaluation metrics, benchmarking, and meta-evaluation for LLMs and RAG systems
· Robustness, factuality, hallucination mitigation, and reliability in generative models
· Retrieval-augmented generation, agentic RAG, and advanced knowledge integration in LLMs
· Applications and real-world use cases of LLM-based systems
Keywords
Large Language Models, Evaluation Frameworks, Generative Models, Knowledge Integration, Retrieval-Augmented Generation, Hallucination Mitigation, Contextual Adaptation
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