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    Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/16951


    Title: Large language models may struggle to detect culturally embedded filicide-suicide risks
    Authors: Chen, CC;Chen, JA;Liang, CS;Lin, YH
    Contributors: Institute of Population Health Sciences
    Abstract: This study examines the capacity of six large language models (LLMs)—GPT-4o, GPT-o1, DeepSeek-R1, Claude 3.5 Sonnet, Sonar Large (LLaMA-3.1), and Gemma-2-2b—to detect risks of domestic violence, suicide, and filicide-suicide in the Taiwanese flash fiction “Barbecue”. The story, narrated by a six-year-old girl, depicts family tension and subtle cues of potential filicide-suicide through charcoal-burning, a culturally recognized method in Taiwan. Each model was tasked with interpreting the story's risks, with roles simulating different mental health expertise levels. Results showed that all models detected domestic violence; however, only GPT-o1, Claude 3.5 Sonnet and Sonar Large identified the risk of suicide based on cultural cues. GPT-4o, DeepSeek-R1 and Gemma-2-2b missed the suicide risk, interpreting the mother's isolation as merely a psychological response. Notably, none of the models comprehended the cultural context behind the mother sparing her daughter, reflecting a gap in LLMs' understanding of non-Western sociocultural nuances. These findings highlight the limitations of LLMs in addressing culturally embedded risks, essential for effective mental health assessments
    Date: 2025-03
    Relation: Asian Journal of Psychiatry. 2025 Mar;105:Article number 104395.
    Link to: http://dx.doi.org/10.1016/j.ajp.2025.104395
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1876-2018&DestApp=IC2JCR
    Cited Times(Scopus): https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85217767323
    Appears in Collections:[林煜軒] 期刊論文

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