报告人：钱林义 教授（华东师范大学）

时间：2025年06月25日 14:00-

地点：数统学院LD402

摘要：This study proposes a comprehensive and generalframework for examining discrepancies in textualcontent using large language models (LLMs), broadening application scenarios in the insurtech and riskmanagement fields, and conducting empirical researchbased on actual needs and real‐world data. Ourframework integrates OpenAI's interface to embedtexts and project them into external categories whileutilizing distance metrics to evaluate discrepancies. Toidentify significant disparities, we design prompts toanalyze three types of relationships: identical information, logical relationships and potential relationships. Our empirical analysis shows that 22.1% ofsamples exhibit substantial semantic discrepancies,and 38.1% of the samples with significant differencescontain at least one of the identified relationships.The average processing time for each sample does notexceed 4 s, and all processes can be adjusted based onactual needs. Backtesting results and comparisons withtraditional NLP methods further demonstrate that ourproposed method is both effective and robust.

邀请人：张志民

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