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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 471480 of 9051 papers

TitleStatusHype
FDS: Feedback-guided Domain Synthesis with Multi-Source Conditional Diffusion Models for Domain GeneralizationCode1
RobocupGym: A challenging continuous control benchmark in RobocupCode1
Emotion and Intent Joint Understanding in Multimodal Conversation: A Benchmarking DatasetCode1
Fibottention: Inceptive Visual Representation Learning with Diverse Attention Across HeadsCode1
RuBLiMP: Russian Benchmark of Linguistic Minimal PairsCode1
Selective Prompting Tuning for Personalized Conversations with LLMsCode1
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
Variationist: Exploring Multifaceted Variation and Bias in Written Language DataCode1
Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based RecommendationCode1
STARD: A Chinese Statute Retrieval Dataset with Real Queries Issued by Non-professionalsCode1
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