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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 27512760 of 9051 papers

TitleStatusHype
Social Intelligence Data Infrastructure: Structuring the Present and Navigating the Future0
TroubleLLM: Align to Red Team Expert0
RORA: Robust Free-Text Rationale EvaluationCode0
Multi-FAct: Assessing Factuality of Multilingual LLMs using FActScoreCode0
Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality EstimationCode2
OccTransformer: Improving BEVFormer for 3D camera-only occupancy prediction0
Improving Open-Ended Text Generation via Adaptive DecodingCode1
Priority Sampling of Large Language Models for Compilers0
Autoencoder-based General Purpose Representation Learning for Customer Embedding0
Ensemble Methodology:Innovations in Credit Default Prediction Using LightGBM, XGBoost, and LocalEnsemble0
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