{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/152819"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/152819","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Generative Models for Domain-Specific Summarization","abstract":"This project evaluates the performance of generative models of summarization in aviation safety domain. Models such as DaVinci, Text-DaVinci-003, and GPT-3.5-Turbo were analyzed in both their zero-shot learning and fine-tuned performance against state-of-the-art models. In zero-shot learning, generative models were superior in most cases to the state-of-the- art models, whereas the fine-tuned models could learn with less information about the dataset. 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Models such as DaVinci, Text-DaVinci-003, and GPT-3.5-Turbo were analyzed in both their zero-shot learning and fine-tuned performance against state-of-the-art models. In zero-shot learning, generative models were superior in most cases to the state-of-the- art models, whereas the fine-tuned models could learn with less information about the dataset. These results predict promising advances in the summarization space to address current limitations in the field."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["M.Eng."]},{"key":"dc:title","label":"Title","values":["Generative Models for Domain-Specific Summarization"]}]}],"canonical_facts":{"dc:contributor.advisor":["Katz, Boris"],"dc:contributor.department":["Massachusetts Institute of Technology. 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