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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 581590 of 9051 papers

TitleStatusHype
Improving Open-Ended Text Generation via Adaptive DecodingCode1
MELoRA: Mini-Ensemble Low-Rank Adapters for Parameter-Efficient Fine-TuningCode1
Taming the Tail in Class-Conditional GANs: Knowledge Sharing via Unconditional Training at Lower ResolutionsCode1
NuNER: Entity Recognition Encoder Pre-training via LLM-Annotated DataCode1
Mirror: A Multiple-perspective Self-Reflection Method for Knowledge-rich ReasoningCode1
MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation SystemsCode1
Visual Hallucinations of Multi-modal Large Language ModelsCode1
Class-Aware Mask-Guided Feature Refinement for Scene Text RecognitionCode1
DSLR: Diversity Enhancement and Structure Learning for Rehearsal-based Graph Continual LearningCode1
LongWanjuan: Towards Systematic Measurement for Long Text QualityCode1
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