SOURCE-LINKED INTELLIGENCE

The Dialect Tax: Dialectal Biases Persist throughout the Language Modeling Pipeline

arXiv · AI, language, vision and robotics · article · Aug 24, 2026 · UTC

Systematic dialectal performance gaps in language models (LMs) are well documented, but the source of these disparities within the modern language modeling pipeline remains unclear. Our study traces this "dialect tax" across the natural language processing pipeline. Using parallel English dialect corpora that hold meaning fixed while varying surface form, we first confirm that LMs recognize matched Standard American English (SAE) and dialectal texts as semantically equivalent. However, we discover further representational gaps corresponding to downstream performance gaps. Across model families

Read original source ↗ Open in workspace

recordType

paper

region

Global