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Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 84 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 152 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 24
Collections
Discover the best community collections!
Collections including paper arxiv:2403.20327
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Improving Text Embeddings with Large Language Models
Paper • 2401.00368 • Published • 83 -
Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training
Paper • 2405.06932 • Published • 21 -
Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper • 2403.20327 • Published • 49 -
Multilingual E5 Text Embeddings: A Technical Report
Paper • 2402.05672 • Published • 23
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Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper • 2403.20327 • Published • 49 -
Round and Round We Go! What makes Rotary Positional Encodings useful?
Paper • 2410.06205 • Published • 2 -
Byte Latent Transformer: Patches Scale Better Than Tokens
Paper • 2412.09871 • Published • 109 -
MrT5: Dynamic Token Merging for Efficient Byte-level Language Models
Paper • 2410.20771 • Published • 3
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MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions
Paper • 2403.19651 • Published • 23 -
No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance
Paper • 2404.04125 • Published • 30 -
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies
Paper • 2404.08197 • Published • 30 -
Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper • 2403.20327 • Published • 49
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Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper • 2403.20327 • Published • 49 -
2D Matryoshka Sentence Embeddings
Paper • 2402.14776 • Published • 6 -
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
Paper • 2412.13663 • Published • 154
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Lumiere: A Space-Time Diffusion Model for Video Generation
Paper • 2401.12945 • Published • 86 -
Long-form factuality in large language models
Paper • 2403.18802 • Published • 27 -
ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion
Paper • 2403.18818 • Published • 29 -
TC4D: Trajectory-Conditioned Text-to-4D Generation
Paper • 2403.17920 • Published • 18
-
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 24 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 84 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 152 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 24
-
Improving Text Embeddings with Large Language Models
Paper • 2401.00368 • Published • 83 -
Piccolo2: General Text Embedding with Multi-task Hybrid Loss Training
Paper • 2405.06932 • Published • 21 -
Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper • 2403.20327 • Published • 49 -
Multilingual E5 Text Embeddings: A Technical Report
Paper • 2402.05672 • Published • 23
-
Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper • 2403.20327 • Published • 49 -
2D Matryoshka Sentence Embeddings
Paper • 2402.14776 • Published • 6 -
Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference
Paper • 2412.13663 • Published • 154
-
Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper • 2403.20327 • Published • 49 -
Round and Round We Go! What makes Rotary Positional Encodings useful?
Paper • 2410.06205 • Published • 2 -
Byte Latent Transformer: Patches Scale Better Than Tokens
Paper • 2412.09871 • Published • 109 -
MrT5: Dynamic Token Merging for Efficient Byte-level Language Models
Paper • 2410.20771 • Published • 3
-
MagicLens: Self-Supervised Image Retrieval with Open-Ended Instructions
Paper • 2403.19651 • Published • 23 -
No "Zero-Shot" Without Exponential Data: Pretraining Concept Frequency Determines Multimodal Model Performance
Paper • 2404.04125 • Published • 30 -
Scaling (Down) CLIP: A Comprehensive Analysis of Data, Architecture, and Training Strategies
Paper • 2404.08197 • Published • 30 -
Gecko: Versatile Text Embeddings Distilled from Large Language Models
Paper • 2403.20327 • Published • 49
-
Lumiere: A Space-Time Diffusion Model for Video Generation
Paper • 2401.12945 • Published • 86 -
Long-form factuality in large language models
Paper • 2403.18802 • Published • 27 -
ObjectDrop: Bootstrapping Counterfactuals for Photorealistic Object Removal and Insertion
Paper • 2403.18818 • Published • 29 -
TC4D: Trajectory-Conditioned Text-to-4D Generation
Paper • 2403.17920 • Published • 18