Embedding models trained on Census surname, first-name, and voter file data improve probabilistic race prediction for uncommon surnames, with full-name embeddings delivering the largest gains especially for Hispanic and Asian individuals.
Predicting Race and Ethnicity from the Sequence of Characters in a Name
5 Pith papers cite this work. Polarity classification is still indexing.
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NameBERT models trained on LLM-augmented academic name data outperform state-of-the-art baselines in nationality classification from names, with augmentation providing gains especially on tail countries.
Audits of 43 LLMs show that varying persona prompts (language, location, role-and-task) and context affects technical quality and social representativeness of scholar recommendations, with location impacting diversity and factuality.
QUIET ranks structurally influential but functionally quiet white-matter edges to identify low-energy pathways for targeted neural synchronization, validated on synthetic networks and HCP data linking control energy to intelligence and sedation states.
citing papers explorer
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Using Embedding Models to Improve Probabilistic Race Prediction
Embedding models trained on Census surname, first-name, and voter file data improve probabilistic race prediction for uncommon surnames, with full-name embeddings delivering the largest gains especially for Hispanic and Asian individuals.
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NameBERT: Scaling Name-Based Nationality Classification with LLM-Augmented Open Academic Data
NameBERT models trained on LLM-augmented academic name data outperform state-of-the-art baselines in nationality classification from names, with augmentation providing gains especially on tail countries.
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Whose Name Comes Up? III: Persona Prompting Effects in LLM-Based Scholar Recommendation
Audits of 43 LLMs show that varying persona prompts (language, location, role-and-task) and context affects technical quality and social representativeness of scholar recommendations, with location impacting diversity and factuality.
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QUIET: Quantifying Underutilized Influential Edges for Targeted Synchronization
QUIET ranks structurally influential but functionally quiet white-matter edges to identify low-energy pathways for targeted neural synchronization, validated on synthetic networks and HCP data linking control energy to intelligence and sedation states.
- War in the Abstract: The Rise and Consequences of Militarized Language in Scientific Communication