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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 631640 of 9051 papers

TitleStatusHype
An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language ModelsCode1
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
A Diverse Corpus for Evaluating and Developing English Math Word Problem SolversCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
Dan: Deep attention neural network for news recommendationCode1
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
Data Augmentation Alone Can Improve Adversarial TrainingCode1
Data Augmentation Approaches in Natural Language Processing: A SurveyCode1
CoT-ICL Lab: A Petri Dish for Studying Chain-of-Thought Learning from In-Context DemonstrationsCode1
An Empirical Study On Contrastive Search And Contrastive Decoding For Open-ended Text GenerationCode1
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