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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

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Showing 1471–1480 of 9051 papers

TitleStatusHype
AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion ModelsCode0
LLM-TOPLA: Efficient LLM Ensemble by Maximising DiversityCode0
Can Language Models Reason about Individualistic Human Values and Preferences?—0
Multilingual Topic Classification in X: Dataset and Analysis—0
Can LLMs Generate Diverse Molecules? Towards Alignment with Structural Diversity—0
Text-guided Diffusion Model for 3D Molecule Generation—0
Multi-Dialect Vietnamese: Task, Dataset, Baseline Models and ChallengesCode0
PersoBench: Benchmarking Personalized Response Generation in Large Language ModelsCode0
Deliberate Reasoning for LLMs as Structure-aware Planning with Accurate World Model—0
Scaling Parameter-Constrained Language Models with Quality Data—0
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