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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 3351–3375 of 9051 papers

TitleStatusHype
Artificial Immune System of Secure Face Recognition Against Adversarial AttacksCode0
Few-shot Personalization of LLMs with Mis-aligned ResponsesCode0
ACD-DE: An adaptive cluster division Differential Evolution for mitigating population diversity deficiencyCode0
Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models—0
Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation—0
Enhancing LLM-Based Human-Robot Interaction with Nuances for Diversity Awareness—0
Encourage or Inhibit Monosemanticity? Revisit Monosemanticity from a Feature Decorrelation PerspectiveCode0
From Distributional to Overton Pluralism: Investigating Large Language Model AlignmentCode0
Application of Liquid Rank Reputation System for Twitter Trend Analysis on Bitcoin—0
Native Design Bias: Studying the Impact of English Nativeness on Language Model PerformanceCode0
Automated Adversarial Discovery for Safety Classifiers—0
tcrLM: a lightweight protein language model for predicting T cell receptor and epitope binding specificityCode0
Investigating the Influence of Prompt-Specific Shortcuts in AI Generated Text DetectionCode0
Repulsive Latent Score Distillation for Solving Inverse ProblemsCode0
Evaluating Visual and Cultural Interpretation: The K-Viscuit Benchmark with Human-VLM Collaboration—0
Enhancing Scientific Figure Captioning Through Cross-modal Learning—0
Towards Comprehensive Preference Data Collection for Reward Modeling—0
The unpaved road towards efficient selective breeding in insects for food and feed—0
Learning k-Determinantal Point Processes for Personalized Ranking—0
Meta-FL: A Novel Meta-Learning Framework for Optimizing Heterogeneous Model Aggregation in Federated Learning—0
Pose-dIVE: Pose-Diversified Augmentation with Diffusion Model for Person Re-Identification—0
video-SALMONN: Speech-Enhanced Audio-Visual Large Language ModelsCode0
Evaluating Diversity in Automatic Poetry GenerationCode0
Unseen Object Reasoning with Shared Appearance CuesCode0
PARIKSHA: A Large-Scale Investigation of Human-LLM Evaluator Agreement on Multilingual and Multi-Cultural Data—0
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