Question-answering from old sources with large language modelsTavchioski, Ilija (Avtor) Robnik Šikonja, Marko (Mentor) Natural Language ProcessingHistorical languageMachine LearningLarge Language ModelsQuestion AnsweringWith the expansion of artificial intelligence and the field of natural language processing, researchers and corporations trained their models on huge text corpora. However, a large portion of historical sources and knowledge remain underutilized due to significant differences in vocabulary, linguistic structure, and writing styles. In this work, we address historical texts and documents in less-resourced languages, focused on Slovenian. By using a digitized corpus of documents in historical Slovenian, we generated a QA (question-answering) dataset using Slovene large language model GaMS (Generative Model for Slovene). We supported our research on historical Slovenian with several methodologies such as fine-tuned large language models, PageIndex RAG (Retrieval-Augmented Generation) and a RAG approach with a hybrid retriever, expanded with different embeddings such as Sentence BERT, F2LLM and our own fine-tuned model. The results show that the GaMS3 model is the most suited for generating a good QA dataset from historical data and is the best performing model for question answering, while the hybrid retriever enhanced with embeddings calculated from our own finetuned model was best suited for retrieval tasks.20262026-05-05 09:35:04Magistrsko delo/naloga182246VisID: 38461COBISS_ID: 277475587sl