Author: Lin, Haolei
Title: Capabilities and limitations of ChatGPT in classifying crane-related accident reports
Advisors: Chi, Hung-lin (BRE)
Degree: M.Sc.
Year: 2023
Subject: Cranes, derricks, etc -- Accidents
Natural language generation (Computer science) -- Computer programs
Artificial intelligence
Hong Kong Polytechnic University -- Dissertations
Department: Department of Building and Real Estate
Pages: iv, 35 pages : color illustrations
Language: English
Abstract: Cranes hold an indispensable and vital role in the realm of construction engineering. Efficiently summarizing and managing the causal factors behind crane-related accidents can significantly mitigate unnecessary hazards. This study focuses on the application of ChatGPT in the classification of crane-related accident reports, summarizing the fundamental framework of utilizing the Prompt for ChatGPT for conducting classification tasks. The aim is to diminish manual labeling time for researchers while constructing their own deep learning models, and concurrently enhance the accuracy of ChatGPT's classification abilities.
This research encompasses two distinct phases. The initial phase entails a preliminary data analysis of crane-related incident reports from 2002 to 2021, sourced from the Occupational Safety and Health Administration (OSHA). Subsequently, ChatGPT is applied to conduct classification experiments, with only the basic requirements of the Prompt being edited. The evaluation metric employed is the F1-Score, and the Prompt improvement focuses on addressing two specific limitations of ChatGPT: the difficulty in classifying due to vague classification standards, and high levels of expertise in labeling.
In the second phase, iterative experiments are carried out by using ChatGPT repeatedly refining and adjusting the Prompts. Ultimately, the study concludes that transforming a single classification task into a composite of multiple classification tasks, endowing ChatGPT with an assumed identity, and allocating adequate computational time are effective methods within the framework for crafting Prompts that enhance the accuracy of ChatGPT's classification performance.
Rights: All rights reserved
Access: restricted access

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