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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 23 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 83 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 146 -
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:2402.19479
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WorldDreamer: Towards General World Models for Video Generation via Predicting Masked Tokens
Paper • 2401.09985 • Published • 16 -
CustomVideo: Customizing Text-to-Video Generation with Multiple Subjects
Paper • 2401.09962 • Published • 9 -
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
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LocalMamba: Visual State Space Model with Windowed Selective Scan
Paper • 2403.09338 • Published • 8 -
GiT: Towards Generalist Vision Transformer through Universal Language Interface
Paper • 2403.09394 • Published • 26 -
Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers
Paper • 2402.19479 • Published • 33 -
Grounding DINO 1.5: Advance the "Edge" of Open-Set Object Detection
Paper • 2405.10300 • Published • 28
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 608 -
MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases
Paper • 2402.14905 • Published • 126 -
YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
Paper • 2402.13616 • Published • 47 -
Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers
Paper • 2402.19479 • Published • 33
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 608 -
Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models
Paper • 2402.17177 • Published • 87 -
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 53 -
Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers
Paper • 2402.19479 • Published • 33
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 608 -
GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
Paper • 2403.03507 • Published • 185 -
Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models
Paper • 2402.19427 • Published • 53 -
ResLoRA: Identity Residual Mapping in Low-Rank Adaption
Paper • 2402.18039 • Published • 11
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Aria Everyday Activities Dataset
Paper • 2402.13349 • Published • 31 -
YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
Paper • 2402.13616 • Published • 47 -
Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teachers
Paper • 2402.19479 • Published • 33 -
Evaluating D-MERIT of Partial-annotation on Information Retrieval
Paper • 2406.16048 • Published • 35
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EVA-CLIP-18B: Scaling CLIP to 18 Billion Parameters
Paper • 2402.04252 • Published • 26 -
Vision Superalignment: Weak-to-Strong Generalization for Vision Foundation Models
Paper • 2402.03749 • Published • 13 -
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Paper • 2402.04615 • Published • 41 -
EfficientViT-SAM: Accelerated Segment Anything Model Without Performance Loss
Paper • 2402.05008 • Published • 22