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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 2001–2025 of 9051 papers

TitleStatusHype
Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment—0
Pictures Of MIDI: Controlled Music Generation via Graphical Prompts for Image-Based Diffusion Inpainting—0
Deep Reinforcement Learning Strategies in Finance: Insights into Asset Holding, Trading Behavior, and Purchase Diversity—0
Iterative Data Generation with Large Language Models for Aspect-based Sentiment Analysis—0
The Factuality Tax of Diversity-Intervened Text-to-Image Generation: Benchmark and Fact-Augmented InterventionCode0
A survey on the impact of AI-based recommenders on human behaviours: methodologies, outcomes and future directions—0
Multi-task multi-constraint differential evolution with elite-guided knowledge transfer for coal mine integrated energy system dispatching—0
PerAct2: Benchmarking and Learning for Robotic Bimanual Manipulation TasksCode2
CMMaTH: A Chinese Multi-modal Math Skill Evaluation Benchmark for Foundation Models—0
Surrogate-assisted evolutionary framework with an ensemble of teaching-learning and differential evolution for expensive optimizationCode0
Detection and Measurement of Syntactic Templates in Generated Text—0
From Local Concepts to Universals: Evaluating the Multicultural Understanding of Vision-Language Models—0
Doc2Token: Bridging Vocabulary Gap by Predicting Missing Tokens for E-commerce Search—0
Optimizing Cyber Defense in Dynamic Active Directories through Reinforcement Learning—0
Paraphrase Types Elicit Prompt Engineering CapabilitiesCode0
Debate-to-Write: A Persona-Driven Multi-Agent Framework for Diverse Argument GenerationCode0
Data Generation Using Large Language Models for Text Classification: An Empirical Case Study—0
The Great AI Witch Hunt: Reviewers Perception and (Mis)Conception of Generative AI in Research Writing—0
OTFS-NOMA System for MIMO Communication Networks with Spatial Diversity—0
Inclusivity in Large Language Models: Personality Traits and Gender Bias in Scientific Abstracts—0
Are Generative Language Models Multicultural? A Study on Hausa Culture and Emotions using ChatGPT—0
Fibottention: Inceptive Visual Representation Learning with Diverse Attention Across HeadsCode1
UniGen: A Unified Framework for Textual Dataset Generation Using Large Language ModelsCode2
EmPO: Emotion Grounding for Empathetic Response Generation through Preference OptimizationCode0
Manipulate-Anything: Automating Real-World Robots using Vision-Language Models—0
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