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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 431440 of 9051 papers

TitleStatusHype
What Large Language Models Do Not Talk About: An Empirical Study of Moderation and Censorship Practices0
BOOST: Bootstrapping Strategy-Driven Reasoning Programs for Program-Guided Fact-Checking0
CHARMS: A Cognitive Hierarchical Agent for Reasoning and Motion Stylization in Autonomous DrivingCode0
MegaMath: Pushing the Limits of Open Math CorporaCode2
Engineering Artificial Intelligence: Framework, Challenges, and Future Direction0
Overcoming Deceptiveness in Fitness Optimization with Unsupervised Quality-DiversityCode0
STAR-1: Safer Alignment of Reasoning LLMs with 1K Data0
OpenCodeReasoning: Advancing Data Distillation for Competitive Coding0
Study of scaling laws in language families0
A Doubly Decoupled Network for edge detectionCode1
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