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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 21212130 of 9051 papers

TitleStatusHype
How Far Can We Extract Diverse Perspectives from Large Language Models?Code0
How Good Are Synthetic Requirements ? Evaluating LLM-Generated Datasets for AI4RECode0
How Does A Text Preprocessing Pipeline Affect Ontology Syntactic Matching?Code0
How Inclusively do LMs Perceive Social and Moral Norms?Code0
IDIAP Submission@LT-EDI-ACL2022 : Hope Speech Detection for Equality, Diversity and InclusionCode0
Open-Domain Question-Answering for COVID-19 and Other Emergent DomainsCode0
Hierarchical Reinforcement Learning via Advantage-Weighted Information MaximizationCode0
Hierarchical Pruning of Deep Ensembles with Focal DiversityCode0
Hierarchical Federated Learning in Multi-hop Cluster-Based VANETsCode0
A Contextual Bandit Bake-offCode0
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