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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 476500 of 9051 papers

TitleStatusHype
Selective Prompting Tuning for Personalized Conversations with LLMsCode1
Variationist: Exploring Multifaceted Variation and Bias in Written Language DataCode1
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionalsCode1
Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based RecommendationCode1
InternLM-Law: An Open Source Chinese Legal Large Language ModelCode1
Voice Disorder Analysis: a Transformer-based ApproachCode1
Advancing Fine-Grained Classification by Structure and Subject Preserving AugmentationCode1
Latent Denoising Diffusion GAN: Faster sampling, Higher image qualityCode1
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity RecognitionCode1
Concept-skill Transferability-based Data Selection for Large Vision-Language ModelsCode1
FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food CultureCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
LUMA: A Benchmark Dataset for Learning from Uncertain and Multimodal DataCode1
A Large-scale Universal Evaluation Benchmark For Face Forgery DetectionCode1
Optimal Kernel Orchestration for Tensor Programs with KorchCode1
MMRel: A Relation Understanding Benchmark in the MLLM EraCode1
REAL Sampling: Boosting Factuality and Diversity of Open-Ended Generation via Asymptotic EntropyCode1
Synthesizing Efficient Data with Diffusion Models for Person Re-Identification Pre-TrainingCode1
MoPS: Modular Story Premise Synthesis for Open-Ended Automatic Story GenerationCode1
Bootstrapping Referring Multi-Object TrackingCode1
Diversified Batch Selection for Training AccelerationCode1
The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed TomographyCode1
CLoG: Benchmarking Continual Learning of Image Generation ModelsCode1
Improving Geo-diversity of Generated Images with Contextualized Vendi Score GuidanceCode1
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