dc.rights.license | CC-BY-NC-ND | |
dc.contributor.advisor | Velegrakis, Ioannis | |
dc.contributor.author | Leal Castillo, Enrique | |
dc.date.accessioned | 2023-07-25T00:01:08Z | |
dc.date.available | 2023-07-25T00:01:08Z | |
dc.date.issued | 2023 | |
dc.identifier.uri | https://studenttheses.uu.nl/handle/20.500.12932/44283 | |
dc.description.abstract | [""The advent of transformer models in natural language processing (NLP), particularly extensive transformer models with billions of parameters often referred to as large language models (LLMs), have made signifi-
cant strides in question-answering (QA) tasks leading to the production
of more convincing natural language responses. Despite these advance- ments, assessing the reliability of QA systems remains challenging due to the intricate nature of language and the diverse array of question types ""] | |
dc.description.sponsorship | Utrecht University | |
dc.language.iso | EN | |
dc.subject | Question Answering systems and chatbots for customer service in the Cloud domain. | |
dc.title | Investigating Open Source Transformer Techniques for Question Answering Systems on Cloud Domain: A Comparison with ChatGPT3.5-Turbo | |
dc.type.content | Master Thesis | |
dc.rights.accessrights | Open Access | |
dc.subject.keywords | Question Answering Systems A.I. Chatbot | |
dc.subject.courseuu | Applied Data Science | |
dc.thesis.id | 20028 | |