| Abstrakt: |
In this study, we conducted multi-label annotation based on textual entailment for text data related to incident cases that occurred at an electric power company, utilizing GPT, a general-purpose LLM, without additional training. The experiment examined GPT's zero-shot textual entailment performance and, for comparison, its one-shot textual entailment performance using prompt engineering. Furthermore, in this study, the abstract category labels for the causes of incidents used in the annotation task were also extracted zero-shot from GPT-4, and these were approved by human participants to determine the labels. The results of the experiment showed that, particularly in the one-shot approach using prompt engineering, GPT exhibited strong generalization capabilities and demonstrated promising performance, approaching the level of human annotators in certain evaluation metrics. However, it was also suggested that when dealing with highly specialized and multifaceted cases like those in this study, careful adjustments in model choice and prompt settings are required. [ABSTRACT FROM AUTHOR] |
