Investigating and combating gender bias in generative large language models

Miriam  Veenhuizen; Andrew O'Malley

doi:10.17576/MH.2024.s1909

Investigating and combating gender bias in generative large language models

^*Corresponding author for this work

Research output: Contribution to journal › Abstract › peer-review

Original language	English
Pages (from-to)	17
Number of pages	1
Journal	Medicine & Health
Volume	19
Issue number	9 (suppl)
DOIs	https://doi.org/10.17576/MH.2024.s1909
Publication status	Published - 30 Dec 2024
Event	1st International Conference on Artificial Intelligence in Medical Education - Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia Duration: 6 Dec 2024 → 8 Dec 2024 Conference number: 1

Keywords

AI safety
Diversity
Large language models

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10.17576/MH.2024.s1909

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title = "Investigating and combating gender bias in generative large language models",

keywords = "AI safety, Diversity, Large language models",

author = "Miriam Veenhuizen and Andrew O'Malley",

year = "2024",

month = dec,

day = "30",

doi = "10.17576/MH.2024.s1909",

language = "English",

volume = "19",

pages = "17",

journal = "Medicine \& Health",

issn = "2289-5728",

number = "9 (suppl)",

note = "1st International Conference on Artificial Intelligence in Medical Education, AIMEC 2024 ; Conference date: 06-12-2024 Through 08-12-2024",

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T1 - Investigating and combating gender bias in generative large language models

AU - Veenhuizen, Miriam

AU - O'Malley, Andrew

N1 - Conference code: 1

PY - 2024/12/30

Y1 - 2024/12/30

KW - AI safety

KW - Diversity

KW - Large language models

UR - https://medicineandhealthukm.com/1st-international-conference-artificial-intelligence-medical-education-maemhs-2024

U2 - 10.17576/MH.2024.s1909

DO - 10.17576/MH.2024.s1909

M3 - Abstract

SN - 2289-5728

VL - 19

SP - 17

JO - Medicine & Health

JF - Medicine & Health

IS - 9 (suppl)

T2 - 1st International Conference on Artificial Intelligence in Medical Education

Y2 - 6 December 2024 through 8 December 2024

ER -

Investigating and combating gender bias in generative large language models

Keywords

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