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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 21012110 of 9051 papers

TitleStatusHype
How Inclusively do LMs Perceive Social and Moral Norms?Code0
Towards control of opinion diversity by introducing zealots into a polarised social groupCode0
A Corpus for Reasoning About Natural Language Grounded in PhotographsCode0
Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality SelectionCode0
How Does A Text Preprocessing Pipeline Affect Ontology Syntactic Matching?Code0
How Far Can We Extract Diverse Perspectives from Large Language Models?Code0
How Predictable Are Large Language Model Capabilities? A Case Study on BIG-benchCode0
A Quality-based Syntactic Template Retriever for Syntactically-controlled Paraphrase GenerationCode0
HiTR: Hierarchical Topic Model Re-estimation for Measuring Topical Diversity of DocumentsCode0
Class Incremental Learning with Multi-Teacher DistillationCode0
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