LLMs Silently Correct African American English: Auditing and Mitigating Dialect Bias via Activation Steering
African American English (AAE), a rule-governed dialect spoken by over 30 million people, is routinely misinterpreted and "corrected" by large language models (LLMs). Across six instruction-tuned LLMs (14B to 70B), we show that state-of-the-art models systematically prefer Standard American English (SAE) continuations even when the preceding context is in AAE, effectively rewriting AAE into SAE. We present an end-to-end framework to audit and mitigate this bias. For auditing, we introduce conditional Dialect Group Invariance (cDGI), which isolates true model bias from translator-induced artifacts, and a feature-level localization analysis that identifies which AAE markers most strongly tr...
| Authors | Huan Wu, Ali Emami, Muhammad Furquan Hassan et al. |
|---|---|
| Primary category | cs.CL |
| Published | 7 July 2026 |
| Last updated at source | 12 October 2026 |
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