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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 14011410 of 9051 papers

TitleStatusHype
Can LLMs Patch Security Issues?Code1
LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form ControlCode1
ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency DistillationCode1
LongWanjuan: Towards Systematic Measurement for Long Text QualityCode1
Can pre-trained models assist in dataset distillation?Code1
LSOIE: A Large-Scale Dataset for Supervised Open Information ExtractionCode1
CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation FrameworkCode1
Contrastive Syn-to-Real GeneralizationCode1
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial ExamplesCode1
Controllable Video Captioning with an Exemplar SentenceCode1
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