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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 32413250 of 9051 papers

TitleStatusHype
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
Safer-Instruct: Aligning Language Models with Automated Preference DataCode1
MC^2: Towards Transparent and Culturally-Aware NLP for Minority Languages in ChinaCode1
UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations0
AI-generated text boundary detection with RoFTCode1
AART: AI-Assisted Red-Teaming with Diverse Data Generation for New LLM-powered Applications0
Peer is Your Pillar: A Data-unbalanced Conditional GANs for Few-shot Image Generation0
Towards Reasoning in Large Language Models via Multi-Agent Peer Review CollaborationCode1
Self-Evolved Diverse Data Sampling for Efficient Instruction TuningCode1
Reimagining Speech: A Scoping Review of Deep Learning-Powered Voice Conversion0
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