Evaluating Large Language Models through Gender and Racial Stereotypes

• URL: http://arxiv.org/abs/2311.14788v1
• Date: Fri, 24 Nov 2023 18:41:16 GMT
• Title: Evaluating Large Language Models through Gender and Racial Stereotypes
• Authors: Ananya Malik
• Abstract summary: We conduct a quality comparative study and establish a framework to evaluate language models under the premise of two kinds of biases: gender and race. We find out that while gender bias has reduced immensely in newer models, as compared to older ones, racial bias still exists.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Language Models have ushered a new age of AI gaining traction within the NLP community as well as amongst the general population. AI's ability to make predictions, generations and its applications in sensitive decision-making scenarios, makes it even more important to study these models for possible biases that may exist and that can be exaggerated. We conduct a quality comparative study and establish a framework to evaluate language models under the premise of two kinds of biases: gender and race, in a professional setting. We find out that while gender bias has reduced immensely in newer models, as compared to older ones, racial bias still exists.

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