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DataComp-LM: In search of the next generation of training sets for language models
Paper • 2406.11794 • Published • 48 -
Training Language Models on Synthetic Edit Sequences Improves Code Synthesis
Paper • 2410.02749 • Published • 12 -
Fewer Truncations Improve Language Modeling
Paper • 2404.10830 • Published • 3 -
How to Train Long-Context Language Models (Effectively)
Paper • 2410.02660 • Published • 1
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Collections including paper arxiv:2406.11794
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Qwen2.5-Coder Technical Report
Paper • 2409.12186 • Published • 131 -
Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement
Paper • 2409.12122 • Published • 2 -
DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
Paper • 2405.04434 • Published • 13 -
DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models
Paper • 2402.03300 • Published • 69
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DataComp-LM: In search of the next generation of training sets for language models
Paper • 2406.11794 • Published • 48 -
Be like a Goldfish, Don't Memorize! Mitigating Memorization in Generative LLMs
Paper • 2406.10209 • Published • 8 -
Transformers Can Do Arithmetic with the Right Embeddings
Paper • 2405.17399 • Published • 51 -
DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models in Code Intelligence
Paper • 2406.11931 • Published • 57
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Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models
Paper • 2402.14848 • Published • 18 -
The Prompt Report: A Systematic Survey of Prompting Techniques
Paper • 2406.06608 • Published • 53 -
CRAG -- Comprehensive RAG Benchmark
Paper • 2406.04744 • Published • 41 -
Transformers meet Neural Algorithmic Reasoners
Paper • 2406.09308 • Published • 43
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MS MARCO Web Search: a Large-scale Information-rich Web Dataset with Millions of Real Click Labels
Paper • 2405.07526 • Published • 17 -
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
Paper • 2405.15613 • Published • 13 -
A Touch, Vision, and Language Dataset for Multimodal Alignment
Paper • 2402.13232 • Published • 13 -
How Do Large Language Models Acquire Factual Knowledge During Pretraining?
Paper • 2406.11813 • Published • 30
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DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model
Paper • 2405.04434 • Published • 13 -
The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale
Paper • 2406.17557 • Published • 86 -
DataComp-LM: In search of the next generation of training sets for language models
Paper • 2406.11794 • Published • 48 -
MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases
Paper • 2402.14905 • Published • 126
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Getting it Right: Improving Spatial Consistency in Text-to-Image Models
Paper • 2404.01197 • Published • 30 -
CosmicMan: A Text-to-Image Foundation Model for Humans
Paper • 2404.01294 • Published • 15 -
mOSCAR: A Large-scale Multilingual and Multimodal Document-level Corpus
Paper • 2406.08707 • Published • 15 -
DataComp-LM: In search of the next generation of training sets for language models
Paper • 2406.11794 • Published • 48
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World Model on Million-Length Video And Language With RingAttention
Paper • 2402.08268 • Published • 36 -
Improving Text Embeddings with Large Language Models
Paper • 2401.00368 • Published • 79 -
Chain-of-Thought Reasoning Without Prompting
Paper • 2402.10200 • Published • 99 -
FiT: Flexible Vision Transformer for Diffusion Model
Paper • 2402.12376 • Published • 48