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Collections including paper arxiv:2402.15000

Can Large Language Models Understand Context?

Paper • 2402.00858 • Published Feb 1 • 20
Efficient Tool Use with Chain-of-Abstraction Reasoning

Paper • 2401.17464 • Published Jan 30 • 15
ReFT: Reasoning with Reinforced Fine-Tuning

Paper • 2401.08967 • Published Jan 17 • 27
The Impact of Reasoning Step Length on Large Language Models

Paper • 2401.04925 • Published Jan 10 • 15

Divide-or-Conquer? Which Part Should You Distill Your LLM?

Paper • 2402.15000 • Published Feb 22 • 22
MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs

Paper • 2402.15627 • Published Feb 23 • 32

Evaluating Very Long-Term Conversational Memory of LLM Agents

Paper • 2402.17753 • Published Feb 27 • 17
StructLM: Towards Building Generalist Models for Structured Knowledge Grounding

Paper • 2402.16671 • Published Feb 26 • 26
Do Large Language Models Latently Perform Multi-Hop Reasoning?

Paper • 2402.16837 • Published Feb 26 • 24
Divide-or-Conquer? Which Part Should You Distill Your LLM?

Paper • 2402.15000 • Published Feb 22 • 22

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Griffin: Mixing Gated Linear Recurrences with Local Attention for Efficient Language Models

Paper • 2402.19427 • Published Feb 29 • 50
Beyond Language Models: Byte Models are Digital World Simulators

Paper • 2402.19155 • Published Feb 29 • 45
StarCoder 2 and The Stack v2: The Next Generation

Paper • 2402.19173 • Published Feb 29 • 125
Simple linear attention language models balance the recall-throughput tradeoff

Paper • 2402.18668 • Published Feb 28 • 17

Divide-or-Conquer? Which Part Should You Distill Your LLM?

Paper • 2402.15000 • Published Feb 22 • 22
Adding NVMe SSDs to Enable and Accelerate 100B Model Fine-tuning on a Single GPU

Paper • 2403.06504 • Published Mar 11 • 52
MoAI: Mixture of All Intelligence for Large Language and Vision Models

Paper • 2403.07508 • Published Mar 12 • 71
AnyV2V: A Plug-and-Play Framework For Any Video-to-Video Editing Tasks

Paper • 2403.14468 • Published Mar 21 • 18

Efficient Tool Use with Chain-of-Abstraction Reasoning

Paper • 2401.17464 • Published Jan 30 • 15
Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation

Paper • 2401.15688 • Published Jan 28 • 10
SliceGPT: Compress Large Language Models by Deleting Rows and Columns

Paper • 2401.15024 • Published Jan 26 • 62
From GPT-4 to Gemini and Beyond: Assessing the Landscape of MLLMs on Generalizability, Trustworthiness and Causality through Four Modalities

Paper • 2401.15071 • Published Jan 26 • 33

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