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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 50215030 of 9051 papers

TitleStatusHype
Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policies0
Retrieval Enhanced Data Augmentation for Question Answering on Privacy Policies0
Retrieval & Fine-Tuning for In-Context Tabular Models0
Retrieval-guided Counterfactual Generation for QA0
Retrieval-guided Counterfactual Generation for QA0
Retrieval-guided Cross-view Image Synthesis0
RetriBooru: Leakage-Free Retrieval of Conditions from Reference Images for Subject-Driven Generation0
Retrieving Examples from Memory for Retrieval Augmented Neural Machine Translation: A Systematic Comparison0
Retrieving Implicit and Explicit Emotional Events Using Large Language Models0
Revealing the Hidden Patterns: A Comparative Study on Profiling Subpopulations of MOOC Students0
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