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DTSTART:19700308T020000
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DTSTAMP:20250626T234541Z
LOCATION:B309
DTSTART;TZID=America/New_York:20241118T140000
DTEND;TZID=America/New_York:20241118T140300
UID:submissions.supercomputing.org_SC24_sess753_ws_whpc114@linklings.com
SUMMARY:Do Large Language Models Reflect Societal Gender Bias? A Comparati
 ve Analysis
DESCRIPTION:Shih-Rou Cheng (National Tsing Hua University, Taiwan); Chia-L
 ee Yang (National Center for High-Performance Computing (NCHC), Taiwan); a
 nd Hung-Hsun Chen (Fu Jen Catholic University)\n\nThis study examines gend
 er bias in LLMs by comparing model-generated responses with those of human
  respondents. A questionnaire based on the Gender Equality Public Opinion 
 Survey was employed, with virtual personas reflecting the demographic dist
 ribution of participants, consistent with the human survey. These personas
  engaged in role-playing scenarios using two distinct LLMs. Statistical an
 alysis identified significant differences between AI models and human surv
 ey data, underscoring the regional specificity of gender equality percepti
 ons and the limitations of LLMs in capturing nuanced social dynamics. Furt
 hermore, the study addresses the potential consequences of over-filtering,
  which may reduce diverse viewpoints, including those of minority groups. 
 These findings highlight the necessity of culturally sensitive bias mitiga
 tion strategies and ensuring diversity when applying LLMs in cultural and 
 social contexts.\n\nTag: Broader Engagement, HPC in Society, Inclusivity\n
 \nRegistration Category: Workshop Reg Pass\n\nSession Chairs: Elsa J. Gons
 iorowski (Lawrence Livermore National Laboratory (LLNL)) and Mozhgan Kabir
 i chimeh (NVIDIA Corporation)\n\n
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