Lost in the Middle: How Language Models Use Long Contexts
Paper • 2307.03172 • Published • 44
🚀 Spinning Up in LLMs
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Efficient Estimation of Word Representations in Vector Space
Paper • 1301.3781 • Published • 8 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 26 -
Attention Is All You Need
Paper • 1706.03762 • Published • 121 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 20 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 251 -
Emergent Abilities of Large Language Models
Paper • 2206.07682 • Published • 3 -
Scaling Laws for Neural Language Models
Paper • 2001.08361 • Published • 10 -
Are Emergent Abilities of Large Language Models a Mirage?
Paper • 2304.15004 • Published • 8 -
Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
Paper • 2201.11903 • Published • 15 -
Judging LLM-as-a-judge with MT-Bench and Chatbot Arena
Paper • 2306.05685 • Published • 42 -
Training Compute-Optimal Large Language Models
Paper • 2203.15556 • Published • 11 -
Neural Machine Translation of Rare Words with Subword Units
Paper • 1508.07909 • Published • 4 -
Jamba: A Hybrid Transformer-Mamba Language Model
Paper • 2403.19887 • Published • 112 -
Mixtral of Experts
Paper • 2401.04088 • Published • 160 -
Mixture-of-Depths: Dynamically allocating compute in transformer-based language models
Paper • 2404.02258 • Published • 108 -
Textbooks Are All You Need
Paper • 2306.11644 • Published • 155 -
Rho-1: Not All Tokens Are What You Need
Paper • 2404.07965 • Published • 94 -
Large Language Models Struggle to Learn Long-Tail Knowledge
Paper • 2211.08411 • Published • 3 -
Large Language Models are Zero-Shot Reasoners
Paper • 2205.11916 • Published • 3