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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 21 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 78 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 140 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
Collections
Discover the best community collections!
Collections including paper arxiv:2404.04125
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No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance
Paper • 2404.04125 • Published • 27 -
CoMat: Aligning Text-to-Image Diffusion Model with Image-to-Text Concept Matching
Paper • 2404.03653 • Published • 32 -
Cross-Attention Makes Inference Cumbersome in Text-to-Image Diffusion Models
Paper • 2404.02747 • Published • 11 -
3D Congealing: 3D-Aware Image Alignment in the Wild
Paper • 2404.02125 • Published • 6
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No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance
Paper • 2404.04125 • Published • 27 -
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies
Paper • 2404.08197 • Published • 27 -
Probing the 3D Awareness of Visual Foundation Models
Paper • 2404.08636 • Published • 11 -
AM-RADIO: Agglomerative Model -- Reduce All Domains Into One
Paper • 2312.06709 • Published • 1
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Program Induction by Rationale Generation : Learning to Solve and Explain Algebraic Word Problems
Paper • 1705.04146 • Published • 1 -
Red Teaming GPT-4V: Are GPT-4V Safe Against Uni/Multi-Modal Jailbreak Attacks?
Paper • 2404.03411 • Published • 8 -
No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance
Paper • 2404.04125 • Published • 27 -
Hydragen: High-Throughput LLM Inference with Shared Prefixes
Paper • 2402.05099 • Published • 18
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MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions
Paper • 2403.19651 • Published • 22 -
No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance
Paper • 2404.04125 • Published • 27 -
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies
Paper • 2404.08197 • Published • 27 -
Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper • 2403.20327 • Published • 47
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Lumiere: A Space-Time Diffusion Model for Video Generation
Paper • 2401.12945 • Published • 85 -
Long-form factuality in large language models
Paper • 2403.18802 • Published • 23 -
ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion
Paper • 2403.18818 • Published • 24 -
TC4D: Trajectory-Conditioned Text-to-4D Generation
Paper • 2403.17920 • Published • 15