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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 1476–1500 of 9051 papers

TitleStatusHype
Can LLMs Generate Diverse Molecules? Towards Alignment with Structural Diversity—0
Multilingual Topic Classification in X: Dataset and Analysis—0
Deliberate Reasoning for LLMs as Structure-aware Planning with Accurate World Model—0
Text-guided Diffusion Model for 3D Molecule Generation—0
Scaling Parameter-Constrained Language Models with Quality Data—0
Correlation and Navigation in the Vocabulary Key Representation Space of Language ModelsCode0
Stochastic Sampling from Deterministic Flow Models—0
Quantifying User Coherence: A Unified Framework for Cross-Domain Recommendation Analysis—0
Breaking the mold: The challenge of large scale MARL specialization—0
QDGset: A Large Scale Grasping Dataset Generated with Quality-Diversity—0
Choices are More Important than Efforts: LLM Enables Efficient Multi-Agent ExplorationCode4
Diffusion Meets Options: Hierarchical Generative Skill Composition for Temporally-Extended Tasks—0
Beyond Squared Error: Exploring Loss Design for Enhanced Training of Generative Flow Networks—0
Curvature Diversity-Driven Deformation and Domain Alignment for Point CloudCode2
Stars, Stripes, and Silicon: Unravelling the ChatGPT's All-American, Monochrome, Cis-centric Bias—0
Leveraging Large Language Models to Enhance Personalized Recommendations in E-commerce—0
Synthio: Augmenting Small-Scale Audio Classification Datasets with Synthetic DataCode1
Generate then Refine: Data Augmentation for Zero-shot Intent DetectionCode0
Unleashing the Power of Large Language Models in Zero-shot Relation Extraction via Self-Prompting—0
PersonaMath: Enhancing Math Reasoning through Persona-Driven Data Augmentation—0
CrowdCounter: A benchmark type-specific multi-target counterspeech datasetCode0
Meta-TTT: A Meta-learning Minimax Framework For Test-Time Training—0
Upcycling Instruction Tuning from Dense to Mixture-of-Experts via Parameter Merging—0
Expected Diverse Utility (EDU): Diverse Bayesian Optimization of Expensive Computer Simulators—0
Style-Specific Neurons for Steering LLMs in Text Style TransferCode1
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