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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

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Showing 411–420 of 9051 papers

TitleStatusHype
Exploring Empty Spaces: Human-in-the-Loop Data AugmentationCode1
Style-Specific Neurons for Steering LLMs in Text Style TransferCode1
OpenKD: Opening Prompt Diversity for Zero- and Few-shot Keypoint DetectionCode1
MemSim: A Bayesian Simulator for Evaluating Memory of LLM-based Personal AssistantsCode1
Holistic Automated Red Teaming for Large Language Models through Top-Down Test Case Generation and Multi-turn InteractionCode1
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance ScalingCode1
Content-aware Tile Generation using Exterior Boundary InpaintingCode1
Massively Multi-Person 3D Human Motion Forecasting with Scene ContextCode1
LOLA -- An Open-Source Massively Multilingual Large Language ModelCode1
Diversify and Conquer: Diversity-Centric Data Selection with Iterative RefinementCode1
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