Side-by-side comparison of intent-equivalent SAE and AAVE tweets significantly exacerbates covert dialect bias in LMs compared to isolated evaluation, with explicit dialect labels worsening the effect further.
Large Language Models Discriminate Against Speakers of G erman Dialects
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CL 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
LLMs show minimal sociodemographic disparities in advice because they infer user demographics poorly from history; conversation topics are the main predictor and act as proxies for groups.
citing papers explorer
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Side-by-side Comparison Amplifies Dialect Bias in Language Models
Side-by-side comparison of intent-equivalent SAE and AAVE tweets significantly exacerbates covert dialect bias in LMs compared to isolated evaluation, with explicit dialect labels worsening the effect further.
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Topics as Proxies for Sociodemographics: How Conversational Context Affects LLM Answers
LLMs show minimal sociodemographic disparities in advice because they infer user demographics poorly from history; conversation topics are the main predictor and act as proxies for groups.