Side-by-side Comparison Amplifies Dialect Bias in Language Models 事件

Side-by-side Comparison Amplifies Dialect Bias in Language Models arXiv:2605.24384v1 Announce Type: new Abstract: Language models (LMs) can exhibit systematic biases against speakers based on variations in their dialects, even in the absence of a dialect label, a behavior known as covert dialect bias. In this work, we quantify covert dialect bias in online discourse by evaluating how LMs associate stereotypical traits (derived from social psychology research on racial bias) with intent-equivale