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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 24712480 of 9051 papers

TitleStatusHype
Chat More If You Like: Dynamic Cue Words Planning to Flow Longer Conversations0
A Factuality and Diversity Reconciled Decoding Method for Knowledge-Grounded Dialogue Generation0
Spin glass model of in-context learning0
Diversity Progress for Goal Selection in Discriminability-Motivated RL0
Diversity-Promoting Bayesian Learning of Latent Variable Models0
Diversity Promoting Online Sampling for Streaming Video Summarization0
ChatGPT Role-play Dataset: Analysis of User Motives and Model Naturalness0
Diversity of What? On the Different Conceptualizations of Diversity in Recommender Systems0
ChatGPT as Linguistic Equalizer? Quantifying LLM-Driven Lexical Shifts in Academic Writing0
A Post-Training Enhanced Optimization Approach for Small Language Models0
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