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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 126–150 of 9051 papers

TitleStatusHype
Trajectory First: A Curriculum for Discovering Diverse Policies—0
An Empirical Study of Group Conformity in Multi-Agent Systems—0
Bregman Centroid Guided Cross-Entropy Method—0
AgentCPM-GUI: Building Mobile-Use Agents with Reinforcement Fine-TuningCode5
Evaluating the Unseen Capabilities: How Many Theorems Do LLMs Know?—0
BASIL: Best-Action Symbolic Interpretable Learning for Evolving Compact RL Policies—0
Quantifying and Reducing Speaker Heterogeneity within the Common Voice Corpus for Phonetic Analysis—0
A note on the Diversity Owen values—0
Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures—0
D2AF: A Dual-Driven Annotation and Filtering Framework for Visual Grounding—0
From Macro to Micro: Probing Dataset Diversity in Language Model Fine-Tuning—0
TRIDENT: Enhancing Large Language Model Safety with Tri-Dimensional Diversified Red-Teaming Data SynthesisCode0
Rehearsal with Auxiliary-Informed Sampling for Audio Deepfake DetectionCode0
A Reward-driven Automated Webshell Malicious-code Generator for Red-teaming—0
Reading Recognition in the Wild—0
Soft Reasoning: Navigating Solution Spaces in Large Language Models through Controlled Embedding Exploration—0
RMoA: Optimizing Mixture-of-Agents through Diversity Maximization and Residual CompensationCode0
HESEIA: A community-based dataset for evaluating social biases in large language models, co-designed in real school settings in Latin America—0
The State of Multilingual LLM Safety Research: From Measuring the Language Gap to Mitigating It—0
When Harry Meets Superman: The Role of The Interlocutor in Persona-Based Dialogue Generation—0
From Invariant Representations to Invariant Data: Provable Robustness to Spurious Correlations via Noisy Counterfactual MatchingCode0
Diversity of Transformer Layers: One Aspect of Parameter Scaling Laws—0
Adaptive Spatial Augmentation for Semi-supervised Semantic Segmentation—0
Generating Diverse Training Samples for Relation Extraction with Large Language Models—0
DiCoFlex: Model-agnostic diverse counterfactuals with flexible control—0
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