Developing prompts to facilitate generative pre-trained transformer classifying decision-errors in flight operations

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dc.contributor.authorLi, Wen-Chin
dc.contributor.authorSaunders, Declan
dc.contributor.authorAmanzadeh, Hamed
dc.date.accessioned2025-01-21T10:40:39Z
dc.date.available2025-01-21T10:40:39Z
dc.date.freetoread2025-01-21
dc.date.issued2024-08-27
dc.description.abstractThe emergence of artificial intelligence (AI) with advanced natural language processing offers promising approaches for enhancing the capacity of textual classification. The aviation industry is increasingly interested in adopting AI to improve efficiency, safety, and cost efficiency. This study explores the potential and challenges of using AI to analyse decision errors in flight operations based on the HFACS framework. In pre-training, the model is trained based on a large amount of data to predict the next word in a sequence which allows the model to learn relationships between the words and their meaning in the accident investigation reports. Initial discoveries demonstrated that the AI model could supply a consistent HFACS framework and populate these dimensions with moderate accuracy. Future research is focused on the development of this HFACS-GPT model through fi-ne-tuning and deep learning, facilitating more reliable and consistent conversations.
dc.description.conferencename2024 Human Systems Integration International Conference HSI 2024
dc.identifier.citationLi W-C, Saunders D, Amanzadeh H. (2024) Developing prompts to facilitate generative pre-trained transformer classifying decision-errors in flight operations. In: Human Systems Integration International Conference HSI 2024, 27 - 29 Aug 2024, Jeju, Korea
dc.identifier.elementsID562360
dc.identifier.urihttps://www.flextechchair.org/HSI2024/event-schedule-1.html
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23407
dc.language.isoen
dc.publisherEasyChair
dc.publisher.urihttps://www.flextechchair.org/HSI2024/event-schedule-1.html
dc.rightsAttribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectArtificial Intelligence
dc.subjectAviation Safety
dc.subjectGenerative Pre-trained Transformer
dc.subjectHuman Factors Analysis and Classification System
dc.subjectLarge Language Model
dc.titleDeveloping prompts to facilitate generative pre-trained transformer classifying decision-errors in flight operations
dc.typeConference paper
dcterms.coverageJeju, Korea
dcterms.temporal.endDate29 Aug 2024
dcterms.temporal.startDate27 Aug 2024

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