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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:2409.18869
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iVideoGPT: Interactive VideoGPTs are Scalable World Models
Paper • 2405.15223 • Published • 12 -
Meteor: Mamba-based Traversal of Rationale for Large Language and Vision Models
Paper • 2405.15574 • Published • 53 -
An Introduction to Vision-Language Modeling
Paper • 2405.17247 • Published • 87 -
Matryoshka Multimodal Models
Paper • 2405.17430 • Published • 31
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Flowing from Words to Pixels: A Framework for Cross-Modality Evolution
Paper • 2412.15213 • Published • 25 -
No More Adam: Learning Rate Scaling at Initialization is All You Need
Paper • 2412.11768 • Published • 41 -
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
Paper • 2412.13663 • Published • 117 -
Autoregressive Video Generation without Vector Quantization
Paper • 2412.14169 • Published • 14
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HtmlRAG: HTML is Better Than Plain Text for Modeling Retrieved Knowledge in RAG Systems
Paper • 2411.02959 • Published • 65 -
GarVerseLOD: High-Fidelity 3D Garment Reconstruction from a Single In-the-Wild Image using a Dataset with Levels of Details
Paper • 2411.03047 • Published • 8 -
MVPaint: Synchronized Multi-View Diffusion for Painting Anything 3D
Paper • 2411.02336 • Published • 23 -
GenXD: Generating Any 3D and 4D Scenes
Paper • 2411.02319 • Published • 20
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 605 -
CLEAR: Character Unlearning in Textual and Visual Modalities
Paper • 2410.18057 • Published • 200 -
Unpacking SDXL Turbo: Interpreting Text-to-Image Models with Sparse Autoencoders
Paper • 2410.22366 • Published • 77 -
Emu3: Next-Token Prediction is All You Need
Paper • 2409.18869 • Published • 94
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Pangea: A Fully Open Multilingual Multimodal LLM for 39 Languages
Paper • 2410.16153 • Published • 44 -
AutoTrain: No-code training for state-of-the-art models
Paper • 2410.15735 • Published • 59 -
The Curse of Multi-Modalities: Evaluating Hallucinations of Large Multimodal Models across Language, Visual, and Audio
Paper • 2410.12787 • Published • 31 -
LEOPARD : A Vision Language Model For Text-Rich Multi-Image Tasks
Paper • 2410.01744 • Published • 26
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Addition is All You Need for Energy-efficient Language Models
Paper • 2410.00907 • Published • 145 -
Emu3: Next-Token Prediction is All You Need
Paper • 2409.18869 • Published • 94 -
An accurate detection is not all you need to combat label noise in web-noisy datasets
Paper • 2407.05528 • Published • 3 -
Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP
Paper • 2407.00402 • Published • 22