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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 28512860 of 9051 papers

TitleStatusHype
Randomness Is All You Need: Semantic Traversal of Problem-Solution Spaces with Large Language ModelsCode0
SPHINX-X: Scaling Data and Parameters for a Family of Multi-modal Large Language ModelsCode7
QGFN: Controllable Greediness with Action ValuesCode1
Multimodal Query Suggestion with Multi-Agent Reinforcement Learning from Human Feedback0
AlphaFold Meets Flow Matching for Generating Protein EnsemblesCode4
On Provable Length and Compositional GeneralizationCode0
Conversational Assistants in Knowledge-Intensive Contexts: An Evaluation of LLM- versus Intent-based Systems0
SALAD-Bench: A Hierarchical and Comprehensive Safety Benchmark for Large Language ModelsCode2
Advancing Video Anomaly Detection: A Concise Review and a New Dataset0
Learning Diverse Policies with Soft Self-Generated Guidance0
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