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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 2176–2200 of 9051 papers

TitleStatusHype
DiffExp: Efficient Exploration in Reward Fine-tuning for Text-to-Image Diffusion Models—0
Mixed Signals: A Diverse Point Cloud Dataset for Heterogeneous LiDAR V2X Collaboration—0
Image compositing is all you need for data augmentation—0
Cascading CMA-ES Instances for Generating Input-diverse Solution BatchesCode0
FragFM: Hierarchical Framework for Efficient Molecule Generation via Fragment-Level Discrete Flow Matching—0
ETS: Efficient Tree Search for Inference-Time ScalingCode0
DiffSampling: Enhancing Diversity and Accuracy in Neural Text Generation—0
Poster: SpiderSim: Multi-Agent Driven Theoretical Cybersecurity Simulation for Industrial DigitalizationCode0
Diversity-driven Data Selection for Language Model Tuning through Sparse Autoencoder—0
Multi-Novelty: Improve the Diversity and Novelty of Contents Generated by Large Language Models via inference-time Multi-Views Brainstorming—0
Understanding and Evaluating Hallucinations in 3D Visual Language Models—0
Thinking Outside the (Gray) Box: A Context-Based Score for Assessing Value and Originality in Neural Text Generation—0
Flow-of-Options: Diversified and Improved LLM Reasoning by Thinking Through OptionsCode0
Aligning Sentence Simplification with ESL Learner's Proficiency for Language AcquisitionCode0
Is Human-Like Text Liked by Humans? Multilingual Human Detection and Preference Against AI—0
On the Computational Tractability of the (Many) Shapley Values—0
Demographic Attributes Prediction from Speech Using WavLM Embeddings—0
Energy-Conscious LLM Decoding: Impact of Text Generation Strategies on GPU Energy Consumption—0
Diversity-Oriented Data Augmentation with Large Language Models—0
InsBank: Evolving Instruction Subset for Ongoing AlignmentCode0
Leave No One Behind: Enhancing Diversity While Maintaining Accuracy in Social RecommendationCode0
Attention Mechanism for LLM-based Agents Dynamic Diffusion under Information Asymmetry—0
The Shrinking Landscape of Linguistic Diversity in the Age of Large Language ModelsCode0
Vendi-RAG: Adaptively Trading-Off Diversity And Quality Significantly Improves Retrieval Augmented Generation With LLMs—0
Diversified Sampling Improves Scaling LLM inference—0
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