DIA-HARM reveals that human-written dialectal English degrades disinformation detector F1 by 1.4-3.6% while AI-generated dialectal content stays stable, with multilingual models generalizing better than monolingual ones.
Hu Linmei, Tianchi Yang, Chuan Shi, Houye Ji, and Xiaoli Li
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A proposed pipeline shows LLMs introduce detectable race and gender biases when summarizing life narratives, creating potential for representational harm in research.
BiMind outperforms existing methods in incorrect information detection by disentangling content and knowledge reasoning with attention geometry adaptation and self-retrieval.
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DIA-HARM: Dialectal Disparities in Harmful Content Detection Across 50 English Dialects
DIA-HARM reveals that human-written dialectal English degrades disinformation detector F1 by 1.4-3.6% while AI-generated dialectal content stays stable, with multilingual models generalizing better than monolingual ones.
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Whose Story Gets Told? Positionality and Bias in LLM Summaries of Life Narratives
A proposed pipeline shows LLMs introduce detectable race and gender biases when summarizing life narratives, creating potential for representational harm in research.
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BiMind: A Dual-Head Reasoning Model with Attention-Geometry Adapter for Incorrect Information Detection
BiMind outperforms existing methods in incorrect information detection by disentangling content and knowledge reasoning with attention geometry adaptation and self-retrieval.