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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 20312040 of 9051 papers

TitleStatusHype
A Closer Look into Mixture-of-Experts in Large Language ModelsCode2
AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha FactorsCode3
Selective Prompting Tuning for Personalized Conversations with LLMsCode1
Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models0
MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge AggregationCode5
Native Design Bias: Studying the Impact of English Nativeness on Language Model PerformanceCode0
Variationist: Exploring Multifaceted Variation and Bias in Written Language DataCode1
Application of Liquid Rank Reputation System for Twitter Trend Analysis on Bitcoin0
Encourage or Inhibit Monosemanticity? Revisit Monosemanticity from a Feature Decorrelation PerspectiveCode0
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
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