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Jul 27
Jul 27
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Discover the best community collections!
Collections including paper arxiv:2104.08691
Collection by
May 27
May 27
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Combining Modular Skills in Multitask Learning
Paper • 2202.13914 • Published • 4 -
The Power of Scale for Parameter-Efficient Prompt Tuning
Paper • 2104.08691 • Published • 9 -
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Paper • 2101.00190 • Published • 6 -
GPT Understands, Too
Paper • 2103.10385 • Published • 7
Collection by
Apr 16
Apr 16
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GaLore: Memory-Efficient LLM Training by Gradient Low-Rank Projection
Paper • 2403.03507 • Published • 182 -
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
Paper • 2205.05638 • Published • 3 -
The Power of Scale for Parameter-Efficient Prompt Tuning
Paper • 2104.08691 • Published • 9 -
In-Context Learning Demonstration Selection via Influence Analysis
Paper • 2402.11750 • Published • 2
Collection by
Mar 22
Mar 22
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It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners
Paper • 2009.07118 • Published • 1 -
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Paper • 2101.00190 • Published • 6 -
The Power of Scale for Parameter-Efficient Prompt Tuning
Paper • 2104.08691 • Published • 9 -
GPT Understands, Too
Paper • 2103.10385 • Published • 7
Collection by
Apr 7
Apr 7
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Efficient Few-Shot Learning Without Prompts
Paper • 2209.11055 • Published • 3 -
Parameter-Efficient Transfer Learning for NLP
Paper • 1902.00751 • Published • 1 -
GPT Understands, Too
Paper • 2103.10385 • Published • 7 -
The Power of Scale for Parameter-Efficient Prompt Tuning
Paper • 2104.08691 • Published • 9
Collection by
Jun 12
Jun 12
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 592 -
When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method
Paper • 2402.17193 • Published • 23 -
Training-Free Long-Context Scaling of Large Language Models
Paper • 2402.17463 • Published • 19 -
The Power of Scale for Parameter-Efficient Prompt Tuning
Paper • 2104.08691 • Published • 9
Collection by
Feb 26
Feb 26
Collection by
Jan 30
Jan 30
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Adaptive Budget Allocation for Parameter-Efficient Fine-Tuning
Paper • 2303.10512 • Published -
Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning
Paper • 2205.05638 • Published • 3 -
LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention
Paper • 2303.16199 • Published • 4 -
FedPara: Low-Rank Hadamard Product for Communication-Efficient Federated Learning
Paper • 2108.06098 • Published • 2
Collection by
Dec 18, 2023
Dec 18, 2023
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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