Опубликована: Янв. 1, 2025
Язык: Английский
Опубликована: Янв. 1, 2025
Язык: Английский
Journal of Risk & Insurance, Год журнала: 2025, Номер unknown
Опубликована: Март 26, 2025
Abstract This study proposes a comprehensive and general framework for examining discrepancies in textual content using large language models (LLMs), broadening application scenarios the insurtech risk management fields, conducting empirical research based on actual needs real‐world data. Our integrates OpenAI's interface to embed texts project them into external categories while utilizing distance metrics evaluate discrepancies. To identify significant disparities, we design prompts analyze three types of relationships: identical information, logical relationships potential relationships. analysis shows that 22.1% samples exhibit substantial semantic discrepancies, 38.1% with differences contain at least one identified The average processing time each sample does not exceed 4 s, all processes can be adjusted needs. Backtesting results comparisons traditional NLP methods further demonstrate our proposed method is both effective robust.
Язык: Английский
Процитировано
0Technology in Society, Год журнала: 2025, Номер unknown, С. 102916 - 102916
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
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