LLMs are more accurate when answers match stereotypes in clear contexts, especially for race-gender combinations, and no tested model shows consistent fairness or reliability across intersectional groups.
In2020 IEEE 36th international conference on data engineering (ICDE)
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Intersectional Fairness in Large Language Models
LLMs are more accurate when answers match stereotypes in clear contexts, especially for race-gender combinations, and no tested model shows consistent fairness or reliability across intersectional groups.