Rectified AI priors, obtained by correcting AI-induced data laws before embedding them in techniques like Dirichlet process priors, reduce bias, improve credible interval coverage, and boost performance in tasks like skin disease classification.
ISBN 979-8-89176-189-6
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 3verdicts
UNVERDICTED 3representative citing papers
Survey of 155 researchers finds 44% observed LLM usage in crowdsourced data, with high awareness but insufficient mitigation efforts.
LLMs show split alignment with human hate speech annotations (strong on explicit attributes, inverted on evaluative ones), and attribute-based ridge regression reconstructs continuous scores with R² up to 0.71.
citing papers explorer
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Supercharging Bayesian Inference with Reliable AI-Informed Priors
Rectified AI priors, obtained by correcting AI-induced data laws before embedding them in techniques like Dirichlet process priors, reduce bias, improve credible interval coverage, and boost performance in tasks like skin disease classification.
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Can Crowdsourcing Survive the LLM Era? A Community Survey on Human Data Collection
Survey of 155 researchers finds 44% observed LLM usage in crowdsourced data, with high awareness but insufficient mitigation efforts.
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Attribute-Based Diagnosis of LLM Alignment with Hate Speech Annotations
LLMs show split alignment with human hate speech annotations (strong on explicit attributes, inverted on evaluative ones), and attribute-based ridge regression reconstructs continuous scores with R² up to 0.71.