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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

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Showing 1491–1500 of 9051 papers

TitleStatusHype
Leveraging Large Language Models to Enhance Personalized Recommendations in E-commerce—0
Synthio: Augmenting Small-Scale Audio Classification Datasets with Synthetic DataCode1
Generate then Refine: Data Augmentation for Zero-shot Intent DetectionCode0
Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting—0
PersonaMath: Enhancing Math Reasoning through Persona-Driven Data Augmentation—0
CrowdCounter: A benchmark type-specific multi-target counterspeech datasetCode0
Meta-TTT: A Meta-learning Minimax Framework For Test-Time Training—0
Upcycling Instruction Tuning from Dense to Mixture-of-Experts via Parameter Merging—0
Expected Diverse Utility (EDU): Diverse Bayesian Optimization of Expensive Computer Simulators—0
Style-Specific Neurons for Steering LLMs in Text Style TransferCode1
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