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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 22912300 of 9051 papers

TitleStatusHype
No Filter: Cultural and Socioeconomic Diversity in Contrastive Vision-Language Models0
DirectMultiStep: Direct Route Generation for Multi-Step RetrosynthesisCode1
Annotation-Efficient Preference Optimization for Language Model AlignmentCode1
A Workbench for Autograding Retrieve/Generate SystemsCode0
Multiple Realizability and the Rise of Deep Learning0
Goals as Reward-Producing ProgramsCode1
Orthogonally Initiated Particle Swarm Optimization with Advanced Mutation for Real-Parameter Optimization0
Spotting AI's Touch: Identifying LLM-Paraphrased Spans in TextCode0
G-DIG: Towards Gradient-based Diverse and High-quality Instruction Data Selection for Machine TranslationCode1
Diverse and Effective Synthetic Data Generation for Adaptable Zero-Shot Dialogue State Tracking0
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