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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 26512660 of 9051 papers

TitleStatusHype
Fine-Grained Detoxification via Instance-Level Prefixes for Large Language ModelsCode0
Diversifying Task-oriented Dialogue Response Generation with Prototype Guided ParaphrasingCode0
Finding A Voice: Evaluating African American Dialect Generation for Chatbot TechnologyCode0
LaiDA: Linguistics-aware In-context Learning with Data Augmentation for Metaphor Components IdentificationCode0
Fine-Grained Spatiotemporal Motion Alignment for Contrastive Video Representation LearningCode0
Fidelity-Enriched Contrastive Search: Reconciling the Faithfulness-Diversity Trade-Off in Text GenerationCode0
Few-Shot Specific Emitter Identification via Hybrid Data Augmentation and Deep Metric LearningCode0
Few-shot Quality-Diversity OptimizationCode0
Few-shot Personalization of LLMs with Mis-aligned ResponsesCode0
Few-shot Image Generation via Masked DiscriminationCode0
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