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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorVelegrakis, Ioannis
dc.contributor.authorLeal Castillo, Enrique
dc.date.accessioned2023-07-25T00:01:08Z
dc.date.available2023-07-25T00:01:08Z
dc.date.issued2023
dc.identifier.urihttps://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.sponsorshipUtrecht University
dc.language.isoEN
dc.subjectQuestion Answering systems and chatbots for customer service in the Cloud domain.
dc.titleInvestigating Open Source Transformer Techniques for Question Answering Systems on Cloud Domain: A Comparison with ChatGPT3.5-Turbo
dc.type.contentMaster Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordsQuestion Answering Systems A.I. Chatbot
dc.subject.courseuuApplied Data Science
dc.thesis.id20028


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