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