AI RESEARCH
Measuring Stereotype and Deviation Biases in Large Language Models
arXiv CS.CL
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ArXi:2508.06649v3 Announce Type: replace Large language models (LLMs) are widely applied across diverse domains, raising concerns about their limitations and potential risks. In this study, we investigate two types of bias that LLMs may display: stereotype bias and deviation bias. Stereotype bias refers to when LLMs consistently associate specific traits with a particular graphic group. Deviation bias reflects the disparity between the graphic distributions extracted from LLM-generated content and real-world graphic distributions.