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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 2001–2010 of 9051 papers

TitleStatusHype
MS-DPPs: Multi-Source Determinantal Point Processes for Contextual Diversity Refinement of Composite Attributes in Text to Image RetrievalCode0
CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language PromptingCode0
IIITT@LT-EDI-EACL2021-Hope Speech Detection: There is always Hope in TransformersCode0
Cross-model Back-translated Distillation for Unsupervised Machine TranslationCode0
CollectiveSFT: Scaling Large Language Models for Chinese Medical Benchmark with Collective Instructions in HealthcareCode0
Curiosity as a Self-Supervised Method to Improve Exploration in De novo Drug DesignCode0
IDIAP_TIET@LT-EDI-ACL2022 : Hope Speech Detection in Social Media using Contextualized BERT with Attention MechanismCode0
Curiosity Driven Exploration of Learned Disentangled Goal SpacesCode0
IDIAP Submission@LT-EDI-ACL2022: Homophobia/Transphobia Detection in social media commentsCode0
IDIAP Submission@LT-EDI-ACL2022 : Hope Speech Detection for Equality, Diversity and InclusionCode0
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