VoxSafeBench reveals that speech language models recognize social norms from text but fail to apply them when acoustic cues like speaker or scene determine the appropriate response.
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LLMs are more accurate when answers match stereotypes in clear contexts, especially for race-gender combinations, and no tested model shows consistent fairness or reliability across intersectional groups.
LLMs persuade effectively in human debates yet fail to comprehend deeper dialogical structures such as argument quality and supporting premises.
Model collapse threatens AI democratization by disproportionately impacting low-resource and marginalized communities through reduced training efficiency and data distributions skewed away from distribution tails.
citing papers explorer
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VoxSafeBench: Not Just What Is Said, but Who, How, and Where
VoxSafeBench reveals that speech language models recognize social norms from text but fail to apply them when acoustic cues like speaker or scene determine the appropriate response.
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Intersectional Fairness in Large Language Models
LLMs are more accurate when answers match stereotypes in clear contexts, especially for race-gender combinations, and no tested model shows consistent fairness or reliability across intersectional groups.
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The Thin Line Between Comprehension and Persuasion in LLMs
LLMs persuade effectively in human debates yet fail to comprehend deeper dialogical structures such as argument quality and supporting premises.
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Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communities
Model collapse threatens AI democratization by disproportionately impacting low-resource and marginalized communities through reduced training efficiency and data distributions skewed away from distribution tails.