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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 28212830 of 9051 papers

TitleStatusHype
Exploring Precision and Recall to assess the quality and diversity of LLMsCode0
Instruction Diversity Drives Generalization To Unseen Tasks0
Python is Not Always the Best Choice: Embracing Multilingual Program of ThoughtsCode0
Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQLCode0
Crafting a Good Prompt or Providing Exemplary Dialogues? A Study of In-Context Learning for Persona-based Dialogue Generation0
How to Train Data-Efficient LLMs0
Symmetry-Breaking Augmentations for Ad Hoc Teamwork0
Inadequacies of Large Language Model Benchmarks in the Era of Generative Artificial Intelligence0
Discovering Sensorimotor Agency in Cellular Automata using Diversity SearchCode0
UMOEA/D: A Multiobjective Evolutionary Algorithm for Uniform Pareto Objectives based on Decomposition0
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