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Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

Showing 30013010 of 9051 papers

TitleStatusHype
ChatGPT as a commenter to the news: can LLMs generate human-like opinions?Code0
PolyNet: A Pursuit of Structural Diversity in Very Deep NetworksCode0
Autoregressive Quantile Networks for Generative ModelingCode0
Evaluating Fairness in Argument RetrievalCode0
Evolutionary bagging for ensemble learningCode0
Autonomous skill discovery with Quality-Diversity and Unsupervised DescriptorsCode0
Evaluating Coherence in Dialogue Systems using EntailmentCode0
Dataset Geography: Mapping Language Data to Language UsersCode0
Evaluating Creative Short Story Generation in Humans and Large Language ModelsCode0
Dataset for the First Evaluation on Chinese Machine Reading ComprehensionCode0
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