Whose Pragmatics? Cultural Grounding as a Bottleneck for Stereotype Detection in Egyptian Arabic Social Media
Résumé
Stereotype detection benchmarks assume that stereotyping occurs through what is said -via lexical co-occurrence between demographic terms and stereotypical attributes.We argue that stereotyping is often conveyed by what is meant: through presupposition, implicature, and speechact framing that leave surface content unchanged while embedding prejudice in the pragmatic layer.We call this phenomenon pragmatic stereotyping.Evaluating GPT-4 and Claude 3.5 Sonnet on a stratified sample of 500 Egyptian Arabic social media comments annotated with a seven-tag sentiment/(im)politeness taxonomy, we find that cultural grounding is the critical bottleneck in detecting pragmatic stereotyping in non-English discourse.About 35% of LLM errors result from cultural grounding gaps, leading to a 15-percentage-point F1 difference between explicit tags (0.81) and implicit tags (0.66).These failures are bidirectional: on the author side, LLMs under-detect prejudice encoded through concessive presupposition and backhanded compliments; on the model side, LLMs apply English-based pragmatic assumptions, misinterpreting genuine polite criticism as sarcasm and positive-intended impoliteness as conflictive.Our five-layer Chainof-Thought diagnostic framework localizes these failures to the culture-dependent inference layers.These results extend stereotype evaluation beyond lexical benchmarks and have direct implications for content moderation pipelines serving Arabicspeaking communities.
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