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Attention Is All You Need
Paper • 1706.03762 • Published • 34 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 11 -
Universal Language Model Fine-tuning for Text Classification
Paper • 1801.06146 • Published • 6 -
Language Models are Few-Shot Learners
Paper • 2005.14165 • Published • 9
Collections
Discover the best community collections!
Collections including paper arxiv:2205.14135
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Simple linear attention language models balance the recall-throughput tradeoff
Paper • 2402.18668 • Published • 17 -
Linear Transformers with Learnable Kernel Functions are Better In-Context Models
Paper • 2402.10644 • Published • 74 -
Repeat After Me: Transformers are Better than State Space Models at Copying
Paper • 2402.01032 • Published • 22 -
Zoology: Measuring and Improving Recall in Efficient Language Models
Paper • 2312.04927 • Published • 2
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Universal Language Model Fine-tuning for Text Classification
Paper • 1801.06146 • Published • 6 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 11 -
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Paper • 2205.14135 • Published • 8 -
SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing
Paper • 1808.06226 • Published • 1
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LoRA: Low-Rank Adaptation of Large Language Models
Paper • 2106.09685 • Published • 24 -
Attention Is All You Need
Paper • 1706.03762 • Published • 34 -
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Paper • 2305.18290 • Published • 37 -
Lost in the Middle: How Language Models Use Long Contexts
Paper • 2307.03172 • Published • 31
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Attention Is All You Need
Paper • 1706.03762 • Published • 34 -
LoRA: Low-Rank Adaptation of Large Language Models
Paper • 2106.09685 • Published • 24 -
Direct Preference Optimization: Your Language Model is Secretly a Reward Model
Paper • 2305.18290 • Published • 37 -
Lost in the Middle: How Language Models Use Long Contexts
Paper • 2307.03172 • Published • 31
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Detecting Pretraining Data from Large Language Models
Paper • 2310.16789 • Published • 9 -
Let's Synthesize Step by Step: Iterative Dataset Synthesis with Large Language Models by Extrapolating Errors from Small Models
Paper • 2310.13671 • Published • 17 -
AutoMix: Automatically Mixing Language Models
Paper • 2310.12963 • Published • 14 -
An Emulator for Fine-Tuning Large Language Models using Small Language Models
Paper • 2310.12962 • Published • 13
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Efficient Memory Management for Large Language Model Serving with PagedAttention
Paper • 2309.06180 • Published • 25 -
LM-Infinite: Simple On-the-Fly Length Generalization for Large Language Models
Paper • 2308.16137 • Published • 38 -
Scaling Transformer to 1M tokens and beyond with RMT
Paper • 2304.11062 • Published • 2 -
DeepSpeed Ulysses: System Optimizations for Enabling Training of Extreme Long Sequence Transformer Models
Paper • 2309.14509 • Published • 16
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Attention Is All You Need
Paper • 1706.03762 • Published • 34 -
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Paper • 2005.11401 • Published • 11 -
LoRA: Low-Rank Adaptation of Large Language Models
Paper • 2106.09685 • Published • 24 -
FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness
Paper • 2205.14135 • Published • 8
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MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning
Paper • 2310.09478 • Published • 15 -
Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams
Paper • 2310.08678 • Published • 11 -
Llama 2: Open Foundation and Fine-Tuned Chat Models
Paper • 2307.09288 • Published • 235 -
LLaMA: Open and Efficient Foundation Language Models
Paper • 2302.13971 • Published • 11