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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 661670 of 9051 papers

TitleStatusHype
Towards Reasoning in Large Language Models via Multi-Agent Peer Review CollaborationCode1
MC^2: Towards Transparent and Culturally-Aware NLP for Minority Languages in ChinaCode1
Can LLMs Patch Security Issues?Code1
IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion ModelsCode1
Florence-2: Advancing a Unified Representation for a Variety of Vision TasksCode1
CloudEval-YAML: A Practical Benchmark for Cloud Configuration GenerationCode1
Resilient Multiple Choice Learning: A learned scoring scheme with application to audio scene analysisCode1
DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Indistinct-Boundary Object SegmentationCode1
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language ModelsCode1
Which Examples to Annotate for In-Context Learning? Towards Effective and Efficient SelectionCode1
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