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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 39713980 of 9051 papers

TitleStatusHype
Drafting Event Schemas using Language Models0
Dolphin: A Challenging and Diverse Benchmark for Arabic NLG0
How Predictable Are Large Language Model Capabilities? A Case Study on BIG-benchCode0
Dior-CVAE: Pre-trained Language Models and Diffusion Priors for Variational Dialog GenerationCode0
Voices of Her: Analyzing Gender Differences in the AI Publication WorldCode0
gRNAde: Geometric Deep Learning for 3D RNA inverse designCode2
CodeInstruct: Empowering Language Models to Edit CodeCode1
ReadMe++: Benchmarking Multilingual Language Models for Multi-Domain Readability AssessmentCode1
Curse of "Low" Dimensionality in Recommender Systems0
Towards Graph-hop Retrieval and Reasoning in Complex Question Answering over Textual Database0
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