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Leveraging GloVe Embeddings to Enhance Memory-Based Language Modeling for Commonsense Reasoning

dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorBosch, Antal van den
dc.contributor.authorArmengol Tapiolas, Jaume
dc.date.accessioned2025-08-21T00:03:18Z
dc.date.available2025-08-21T00:03:18Z
dc.date.issued2025
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/49844
dc.description.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectThis thesis explores integrating GloVe word embeddings into Memory-Based Language Models (MBLMs) to enhance performance on Next Word Prediction and Commonsense Reasoning. The study highlights the potential of MBLMs for eco-friendly, interpretable AI.
dc.titleLeveraging GloVe Embeddings to Enhance Memory-Based Language Modeling for Commonsense Reasoning
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.courseuuApplied Data Science
dc.thesis.id52093


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