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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 4926–4950 of 9051 papers

TitleStatusHype
Language Model Evaluation in Open-ended Text Generation—0
Active Collaborative Ensemble Tracking—0
Language Plays a Pivotal Role in the Object-Attribute Compositional Generalization of CLIP—0
Language Resource Building and English-to-Mizo Neural Machine Translation Encountering Tonal Words—0
Language Resources to Support Language Diversity – the ELRA Achievements—0
Languagesindanger.eu - Including Multimedia Language Resources to disseminate Knowledge and Create Educational Material on less-Resourced Languages—0
Language statistics at different spatial, temporal, and grammatical scales—0
Language-Universal Phonetic Representation in Multilingual Speech Pretraining for Low-Resource Speech Recognition—0
LANS: Large-scale Arabic News Summarization Corpus—0
LaPIG: Cross-Modal Generation of Paired Thermal and Visible Facial Images—0
Large Intelligent Surface Assisted Non-Orthogonal Multiple Access: Performance Analysis—0
Benchmarking Active Learning Strategies for Materials Optimization and Discovery—0
Large Language Model as a Universal Clinical Multi-task Decoder—0
BENCHIP: Benchmarking Intelligence Processors—0
Large language model as user daily behavior data generator: balancing population diversity and individual personality—0
Large Language Model-Enhanced Symbolic Reasoning for Knowledge Base Completion—0
Large Language Model-Informed Feature Discovery Improves Prediction and Interpretation of Credibility Perceptions of Visual Content—0
Large Language Models as Conversational Movie Recommenders: A User Study—0
Large Language Models as In-context AI Generators for Quality-Diversity—0
Large Language Models as Partners in Student Essay Evaluation—0
Large Language Models as User-Agents for Evaluating Task-Oriented-Dialogue Systems—0
Large Language Models can Achieve Social Balance—0
BENCHAGENTS: Automated Benchmark Creation with Agent Interaction—0
Large Language Models for Mathematical Reasoning: Progresses and Challenges—0
Large Language Models Know What Makes Exemplary Contexts—0
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