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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 2026–2050 of 9051 papers

TitleStatusHype
EmPO: Emotion Grounding for Empathetic Response Generation through Preference OptimizationCode0
Manipulate-Anything: Automating Real-World Robots using Vision-Language Models—0
ACD-DE: An adaptive cluster division Differential Evolution for mitigating population diversity deficiencyCode0
Artificial Immune System of Secure Face Recognition Against Adversarial AttacksCode0
Few-shot Personalization of LLMs with Mis-aligned ResponsesCode0
A Closer Look into Mixture-of-Experts in Large Language ModelsCode2
AlphaForge: A Framework to Mine and Dynamically Combine Formulaic Alpha FactorsCode3
Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models—0
Selective Prompting Tuning for Personalized Conversations with LLMsCode1
From Distributional to Overton Pluralism: Investigating Large Language Model AlignmentCode0
Enhancing LLM-Based Human-Robot Interaction with Nuances for Diversity Awareness—0
Math-LLaVA: Bootstrapping Mathematical Reasoning for Multimodal Large Language ModelsCode2
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
Encourage or Inhibit Monosemanticity? Revisit Monosemanticity from a Feature Decorrelation PerspectiveCode0
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
Leveraging Synthetic Audio Data for End-to-End Low-Resource Speech Translation—0
MedCare: Advancing Medical LLMs through Decoupling Clinical Alignment and Knowledge AggregationCode5
Variationist: Exploring Multifaceted Variation and Bias in Written Language DataCode1
Automated Adversarial Discovery for Safety Classifiers—0
Enhancing Scientific Figure Captioning Through Cross-modal Learning—0
tcrLM: a lightweight protein language model for predicting T cell receptor and epitope binding specificityCode0
The unpaved road towards efficient selective breeding in insects for food and feed—0
Evaluating Visual and Cultural Interpretation: The K-Viscuit Benchmark with Human-VLM Collaboration—0
Investigating the Influence of Prompt-Specific Shortcuts in AI Generated Text DetectionCode0
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