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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 3140 of 9051 papers

TitleStatusHype
Cross-Layer Discrete Concept Discovery for Interpreting Language Models0
Benchmarking histopathology foundation models in a multi-center dataset for skin cancer subtypingCode0
AdapThink: Adaptive Thinking Preferences for Reasoning Language Model0
Statistical Multicriteria Evaluation of LLM-Generated TextCode0
PlanMoGPT: Flow-Enhanced Progressive Planning for Text to Motion Synthesis0
Rethinking Ecological Measures Of Functional Diversity0
Perceptual Rationality: An Evolutionary Game Theory of Perceptually Rational Decision-Making0
MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based AgentsCode2
AI's Blind Spots: Geographic Knowledge and Diversity Deficit in Generated Urban Scenario0
State-Space Models in Efficient Whispered and Multi-dialect Speech Recognition0
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