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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 14011410 of 9051 papers

TitleStatusHype
Can LLMs Patch Security Issues?Code1
Cerbero-7B: A Leap Forward in Language-Specific LLMs Through Enhanced Chat Corpus Generation and EvaluationCode1
HIVE: Evaluating the Human Interpretability of Visual ExplanationsCode1
Personalized Privacy Protection Mask Against Unauthorized Facial RecognitionCode1
Can pre-trained models assist in dataset distillation?Code1
Domain-Smoothing Network for Zero-Shot Sketch-Based Image RetrievalCode1
ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency DistillationCode1
CETN: Contrast-enhanced Through Network for CTR PredictionCode1
Holistic Automated Red Teaming for Large Language Models through Top-Down Test Case Generation and Multi-turn InteractionCode1
How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality DataCode1
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