Leveraging GloVe Embeddings to Enhance Memory-Based Language Modeling for Commonsense Reasoning
dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Bosch, Antal van den | |
dc.contributor.author | Armengol Tapiolas, Jaume | |
dc.date.accessioned | 2025-08-21T00:03:18Z | |
dc.date.available | 2025-08-21T00:03:18Z | |
dc.date.issued | 2025 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/49844 | |
dc.description.sponsorship | Utrecht University | |
dc.language.iso | EN | |
dc.subject | This 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.title | Leveraging GloVe Embeddings to Enhance Memory-Based Language Modeling for Commonsense Reasoning | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.courseuu | Applied Data Science | |
dc.thesis.id | 52093 |