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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 • 85 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
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:2410.22366
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Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training
Paper • 2502.03460 • Published -
LLM-Pruner: On the Structural Pruning of Large Language Models
Paper • 2305.11627 • Published • 3 -
Pruning as a Domain-specific LLM Extractor
Paper • 2405.06275 • Published • 1 -
KnowTuning: Knowledge-aware Fine-tuning for Large Language Models
Paper • 2402.11176 • Published • 2
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HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems
Paper • 2411.02959 • Published • 71 -
GarVerseLOD: High-Fidelity 3D Garment Reconstruction from a Single In-the-Wild Image using a Dataset with Levels of Details
Paper • 2411.03047 • Published • 9 -
MVPaint: Synchronized Multi-View Diffusion for Painting Anything 3D
Paper • 2411.02336 • Published • 24 -
GenXD: Generating Any 3D and 4D Scenes
Paper • 2411.02319 • Published • 20
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Animate-X: Universal Character Image Animation with Enhanced Motion Representation
Paper • 2410.10306 • Published • 56 -
ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning
Paper • 2411.05003 • Published • 71 -
TIP-I2V: A Million-Scale Real Text and Image Prompt Dataset for Image-to-Video Generation
Paper • 2411.04709 • Published • 26 -
IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation
Paper • 2410.07171 • Published • 43
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Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis
Paper • 2401.09048 • Published • 10 -
Improving fine-grained understanding in image-text pre-training
Paper • 2401.09865 • Published • 18 -
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Paper • 2401.10891 • Published • 62 -
Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild
Paper • 2401.13627 • Published • 78
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ROICtrl: Boosting Instance Control for Visual Generation
Paper • 2411.17949 • Published • 87 -
Unpacking SDXL Turbo: Interpreting Text-to-Image Models with Sparse Autoencoders
Paper • 2410.22366 • Published • 84 -
Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens
Paper • 2410.13863 • Published • 37 -
CoRe: Context-Regularized Text Embedding Learning for Text-to-Image Personalization
Paper • 2408.15914 • Published • 24
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 627 -
CLEAR: Character Unlearning in Textual and Visual Modalities
Paper • 2410.18057 • Published • 209 -
Unpacking SDXL Turbo: Interpreting Text-to-Image Models with Sparse Autoencoders
Paper • 2410.22366 • Published • 84 -
Emu3: Next-Token Prediction is All You Need
Paper • 2409.18869 • Published • 96
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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 • 85 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
-
Compose and Conquer: Diffusion-Based 3D Depth Aware Composable Image Synthesis
Paper • 2401.09048 • Published • 10 -
Improving fine-grained understanding in image-text pre-training
Paper • 2401.09865 • Published • 18 -
Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data
Paper • 2401.10891 • Published • 62 -
Scaling Up to Excellence: Practicing Model Scaling for Photo-Realistic Image Restoration In the Wild
Paper • 2401.13627 • Published • 78
-
Adapt-Pruner: Adaptive Structural Pruning for Efficient Small Language Model Training
Paper • 2502.03460 • Published -
LLM-Pruner: On the Structural Pruning of Large Language Models
Paper • 2305.11627 • Published • 3 -
Pruning as a Domain-specific LLM Extractor
Paper • 2405.06275 • Published • 1 -
KnowTuning: Knowledge-aware Fine-tuning for Large Language Models
Paper • 2402.11176 • Published • 2
-
ROICtrl: Boosting Instance Control for Visual Generation
Paper • 2411.17949 • Published • 87 -
Unpacking SDXL Turbo: Interpreting Text-to-Image Models with Sparse Autoencoders
Paper • 2410.22366 • Published • 84 -
Fluid: Scaling Autoregressive Text-to-image Generative Models with Continuous Tokens
Paper • 2410.13863 • Published • 37 -
CoRe: Context-Regularized Text Embedding Learning for Text-to-Image Personalization
Paper • 2408.15914 • Published • 24
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HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems
Paper • 2411.02959 • Published • 71 -
GarVerseLOD: High-Fidelity 3D Garment Reconstruction from a Single In-the-Wild Image using a Dataset with Levels of Details
Paper • 2411.03047 • Published • 9 -
MVPaint: Synchronized Multi-View Diffusion for Painting Anything 3D
Paper • 2411.02336 • Published • 24 -
GenXD: Generating Any 3D and 4D Scenes
Paper • 2411.02319 • Published • 20
-
Animate-X: Universal Character Image Animation with Enhanced Motion Representation
Paper • 2410.10306 • Published • 56 -
ReCapture: Generative Video Camera Controls for User-Provided Videos using Masked Video Fine-Tuning
Paper • 2411.05003 • Published • 71 -
TIP-I2V: A Million-Scale Real Text and Image Prompt Dataset for Image-to-Video Generation
Paper • 2411.04709 • Published • 26 -
IterComp: Iterative Composition-Aware Feedback Learning from Model Gallery for Text-to-Image Generation
Paper • 2410.07171 • Published • 43
-
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 627 -
CLEAR: Character Unlearning in Textual and Visual Modalities
Paper • 2410.18057 • Published • 209 -
Unpacking SDXL Turbo: Interpreting Text-to-Image Models with Sparse Autoencoders
Paper • 2410.22366 • Published • 84 -
Emu3: Next-Token Prediction is All You Need
Paper • 2409.18869 • Published • 96