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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 22 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 82 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 145 -
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:2410.04364
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Controllable Text Generation for Large Language Models: A Survey
Paper • 2408.12599 • Published • 64 -
xGen-VideoSyn-1: High-fidelity Text-to-Video Synthesis with Compressed Representations
Paper • 2408.12590 • Published • 35 -
Real-Time Video Generation with Pyramid Attention Broadcast
Paper • 2408.12588 • Published • 16 -
Transfusion: Predict the Next Token and Diffuse Images with One Multi-Modal Model
Paper • 2408.11039 • Published • 58
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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 25 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 12 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 40 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 20
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WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens
Paper • 2401.09985 • Published • 15 -
CustomVideo: Customizing Text-to-Video Generation with Multiple Subjects
Paper • 2401.09962 • Published • 8 -
Inflation with Diffusion: Efficient Temporal Adaptation for Text-to-Video Super-Resolution
Paper • 2401.10404 • Published • 10 -
ActAnywhere: Subject-Aware Video Background Generation
Paper • 2401.10822 • Published • 13