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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 3041–3050 of 9051 papers

TitleStatusHype
Diversity as a Reward: Fine-Tuning LLMs on a Mixture of Domain-Undetermined Data—0
Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review—0
Efficient nonmyopic batch active search—0
Diversity as a By-Product: Goal-oriented Language Generation Leads to Linguistic Variation—0
”Diversity and Uncertainty in Moderation” are the Key to Data Selection for Multilingual Few-shot Transfer—0
An LLM-Empowered Adaptive Evolutionary Algorithm For Multi-Component Deep Learning Systems—0
Cifu: a Frequency Lexicon of Hong Kong Cantonese—0
Evaluating the Diversity and Quality of LLM Generated Content—0
Efficient Sampling for k-Determinantal Point Processes—0
Evaluating the Supervised and Zero-shot Performance of Multi-lingual Translation Models—0
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