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Collections including paper arxiv:2410.19290

Rethinking Data Selection at Scale: Random Selection is Almost All You Need

Paper • 2410.09335 • Published Oct 12 • 14
From Generalist to Specialist: Adapting Vision Language Models via Task-Specific Visual Instruction Tuning

Paper • 2410.06456 • Published Oct 9 • 35
Emergent properties with repeated examples

Paper • 2410.07041 • Published Oct 9 • 8
Personalized Visual Instruction Tuning

Paper • 2410.07113 • Published Oct 9 • 69

Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning

Paper • 2410.19290 • Published 25 days ago • 10

about 24 hours ago

RetrievalAttention: Accelerating Long-Context LLM Inference via Vector Retrieval

Paper • 2409.10516 • Published Sep 16 • 37
Measuring and Enhancing Trustworthiness of LLMs in RAG through Grounded Attributions and Learning to Refuse

Paper • 2409.11242 • Published Sep 17 • 5
Promptriever: Instruction-Trained Retrievers Can Be Prompted Like Language Models

Paper • 2409.11136 • Published Sep 17 • 21
On the Diagram of Thought

Paper • 2409.10038 • Published Sep 16 • 11

LinFusion: 1 GPU, 1 Minute, 16K Image

Paper • 2409.02097 • Published Sep 3 • 31
Phidias: A Generative Model for Creating 3D Content from Text, Image, and 3D Conditions with Reference-Augmented Diffusion

Paper • 2409.11406 • Published Sep 17 • 25
Diffusion Models Are Real-Time Game Engines

Paper • 2408.14837 • Published Aug 27 • 121
Segment Anything with Multiple Modalities

Paper • 2408.09085 • Published Aug 17 • 21

Best Practices and Lessons Learned on Synthetic Data for Language Models

Paper • 2404.07503 • Published Apr 11 • 29
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences

Paper • 2404.03715 • Published Apr 4 • 60
Magpie: Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing

Paper • 2406.08464 • Published Jun 12 • 65
Synthetic Data (Almost) from Scratch: Generalized Instruction Tuning for Language Models

Paper • 2402.13064 • Published Feb 20 • 47

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