A Survey on Post-training of Large Language Models
Paper • 2503.06072 • Published • 11
Mei dianwen
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A Review on the Evolvement of Load Balancing Strategy in MoE LLMs: Pitfalls and Lessons
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papers about 2 years ago
SnapKV: LLM Knows What You are Looking for Before Generation
Paper • 2404.14469 • Published • 27
Pre-training Small Base LMs with Fewer Tokens
Paper • 2404.08634 • Published • 36
LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders
Paper • 2404.05961 • Published • 66
Leave No Context Behind: Efficient Infinite Context Transformers with Infini-attention
Paper • 2404.07143 • Published • 111
OmniFusion Technical Report
Paper • 2404.06212 • Published • 77
Chinese Tiny LLM: Pretraining a Chinese-Centric Large Language Model
Paper • 2404.04167 • Published • 13
Stream of Search (SoS): Learning to Search in Language
Paper • 2404.03683 • Published • 30
Visual Autoregressive Modeling: Scalable Image Generation via Next-Scale Prediction
Paper • 2404.02905 • Published • 74
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
Paper • 2404.02258 • Published • 108
Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 112
Advancing LLM Reasoning Generalists with Preference Trees
Paper • 2404.02078 • Published • 46
Long-context LLMs Struggle with Long In-context Learning
Paper • 2404.02060 • Published • 37
Long-form factuality in large language models
Paper • 2403.18802 • Published • 26
ViTAR: Vision Transformer with Any Resolution
Paper • 2403.18361 • Published • 55
Mini-Gemini: Mining the Potential of Multi-modality Vision Language Models
Paper • 2403.18814 • Published • 48