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Attention Is All You Need
Paper • 1706.03762 • Published • 41 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 14 -
Universal Language Model Fine-tuning for Text Classification
Paper • 1801.06146 • Published • 6 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 11
Collections
Discover the best community collections!
Collections including paper arxiv:2005.14165
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SciLitLLM: How to Adapt LLMs for Scientific Literature Understanding
Paper • 2408.15545 • Published • 32 -
Controllable Text Generation for Large Language Models: A Survey
Paper • 2408.12599 • Published • 61 -
To Code, or Not To Code? Exploring Impact of Code in Pre-training
Paper • 2408.10914 • Published • 39 -
Automated Design of Agentic Systems
Paper • 2408.08435 • Published • 37
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Attention Is All You Need
Paper • 1706.03762 • Published • 41 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 11 -
GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints
Paper • 2305.13245 • Published • 5 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 239
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RoFormer: Enhanced Transformer with Rotary Position Embedding
Paper • 2104.09864 • Published • 9 -
Attention Is All You Need
Paper • 1706.03762 • Published • 41 -
LoRA: Low-Rank Adaptation of Large Language Models
Paper • 2106.09685 • Published • 29 -
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Paper • 2205.14135 • Published • 9
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Attention Is All You Need
Paper • 1706.03762 • Published • 41 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 14 -
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
Paper • 1910.01108 • Published • 14 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 11
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Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
Paper • 2404.02258 • Published • 103 -
Textbooks Are All You Need
Paper • 2306.11644 • Published • 142 -
Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 103 -
Large Language Models Struggle to Learn Long-Tail Knowledge
Paper • 2211.08411 • Published • 3
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Long-form factuality in large language models
Paper • 2403.18802 • Published • 23 -
Attention Is All You Need
Paper • 1706.03762 • Published • 41 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 11 -
A Survey of GPT-3 Family Large Language Models Including ChatGPT and GPT-4
Paper • 2310.12321 • Published • 1
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 14 -
RoBERTa: A Robustly Optimized BERT Pretraining Approach
Paper • 1907.11692 • Published • 7 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 11 -
OPT: Open Pre-trained Transformer Language Models
Paper • 2205.01068 • Published • 2
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Lost in the Middle: How Language Models Use Long Contexts
Paper • 2307.03172 • Published • 35 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 14 -
Attention Is All You Need
Paper • 1706.03762 • Published • 41 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 239
-
Attention Is All You Need
Paper • 1706.03762 • Published • 41 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 14 -
Universal Language Model Fine-tuning for Text Classification
Paper • 1801.06146 • Published • 6 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 11