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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 18211830 of 9051 papers

TitleStatusHype
Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-TranslationCode0
Improving the Diversity of Unsupervised Paraphrasing with Embedding OutputsCode0
Improving Linguistic Diversity of Large Language Models with Possibility Exploration Fine-TuningCode0
ASEM: Enhancing Empathy in Chatbot through Attention-based Sentiment and Emotion ModelingCode0
A Grid-Based Evolutionary Algorithm for Many-Objective OptimizationCode0
Improving Neural Conversational Models with Entropy-Based Data FilteringCode0
Improving Generalization with Domain Convex GameCode0
Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation SystemsCode0
ABEX: Data Augmentation for Low-Resource NLU via Expanding Abstract DescriptionsCode0
Improving Language Generation with Sentence Coherence ObjectiveCode0
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