PluRule is a new multimodal multilingual benchmark showing that state-of-the-art vision-language models perform only marginally better than a trivial baseline at detecting specific rule violations in pluralistic online communities.
Demographic Dialectal Variation in Social Media: A Case Study of A frican- A merican E nglish
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ASR bias causes users from underrepresented dialects to internalize failures as personal inadequacy and perform extensive emotional and linguistic labor, revealing harms missed by accuracy-only evaluations.
ArabCulture-Dialogue dataset shows LLMs perform worse on dialectal Arabic than Modern Standard Arabic across cultural reasoning, translation, and generation tasks.
The authors provide a detailed taxonomy of 21 risks associated with language models, covering discrimination, information leaks, misinformation, malicious applications, interaction harms, and societal impacts like job loss and environmental costs.
Incidental multilingualism from uneven web training makes LLMs unequal, brittle, and opaque across languages.
citing papers explorer
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PluRule: A Benchmark for Moderating Pluralistic Communities on Social Media
PluRule is a new multimodal multilingual benchmark showing that state-of-the-art vision-language models perform only marginally better than a trivial baseline at detecting specific rule violations in pluralistic online communities.
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"This Wasn't Made for Me": Recentering User Experience and Emotional Impact in the Evaluation of ASR Bias
ASR bias causes users from underrepresented dialects to internalize failures as personal inadequacy and perform extensive emotional and linguistic labor, revealing harms missed by accuracy-only evaluations.
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Cultural Benchmarking of LLMs in Standard and Dialectal Arabic Dialogues
ArabCulture-Dialogue dataset shows LLMs perform worse on dialectal Arabic than Modern Standard Arabic across cultural reasoning, translation, and generation tasks.
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Ethical and social risks of harm from Language Models
The authors provide a detailed taxonomy of 21 risks associated with language models, covering discrimination, information leaks, misinformation, malicious applications, interaction harms, and societal impacts like job loss and environmental costs.
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Lost in the Tower of Babel: The Adverse Effects of Incidental Multilingualism in LLMs
Incidental multilingualism from uneven web training makes LLMs unequal, brittle, and opaque across languages.