Per-Entity Bias Mapping claims aggregate visibility metrics fail because large brands exhibit higher fabricated citation rates than smaller ones in AI responses, attributed to the Brand Hallucination Paradox.
arXiv preprint arXiv:2108.05412 (2021)
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The study introduces a method for detecting and categorizing cross-lingual factual inconsistencies in Wikipedia tables using alignment techniques and metrics on sample data.
citing papers explorer
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Per-Entity Bias Mapping for AI Visibility: Why Brand Mentions Require Entity-Specific Calibration
Per-Entity Bias Mapping claims aggregate visibility metrics fail because large brands exhibit higher fabricated citation rates than smaller ones in AI responses, attributed to the Brand Hallucination Paradox.
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Factual Inconsistencies in Multilingual Wikipedia Tables
The study introduces a method for detecting and categorizing cross-lingual factual inconsistencies in Wikipedia tables using alignment techniques and metrics on sample data.