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Oct 21, 2025
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Discover the best community collections!
Collections including paper arxiv:2104.08691
Collection by 
Mar 29, 2025
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Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
Paper • 2211.04325 • Published • 1 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 26 -
On the Opportunities and Risks of Foundation Models
Paper • 2108.07258 • Published • 2 -
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Paper • 2204.07705 • Published • 2
Collection by 
Oct 31, 2024
Collection by 
May 27, 2024
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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 • 10 -
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Paper • 2101.00190 • Published • 6 -
GPT Understands, Too
Paper • 2103.10385 • Published • 11
Collection by 
Jun 12, 2024
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The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 628 -
When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method
Paper • 2402.17193 • Published • 26 -
Training-Free Long-Context Scaling of Large Language Models
Paper • 2402.17463 • Published • 24 -
The Power of Scale for Parameter-Efficient Prompt Tuning
Paper • 2104.08691 • Published • 10
Collection by 
Feb 27
Collection by 
Aug 29, 2025
Collection by 
Jul 27, 2024
Collection by 
Apr 7, 2024
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Efficient Few-Shot Learning Without Prompts
Paper • 2209.11055 • Published • 5 -
Parameter-Efficient Transfer Learning for NLP
Paper • 1902.00751 • Published • 2 -
GPT Understands, Too
Paper • 2103.10385 • Published • 11 -
The Power of Scale for Parameter-Efficient Prompt Tuning
Paper • 2104.08691 • Published • 10
Collection by 
Feb 26, 2024
Collection by Oct 21, 2025
Collection by Feb 27
Collection by Mar 29, 2025
-
Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning
Paper • 2211.04325 • Published • 1 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 26 -
On the Opportunities and Risks of Foundation Models
Paper • 2108.07258 • Published • 2 -
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Paper • 2204.07705 • Published • 2
Collection by Aug 29, 2025
Collection by Oct 31, 2024
Collection by Jul 27, 2024
Collection by May 27, 2024
-
Combining Modular Skills in Multitask Learning
Paper • 2202.13914 • Published • 4 -
The Power of Scale for Parameter-Efficient Prompt Tuning
Paper • 2104.08691 • Published • 10 -
Prefix-Tuning: Optimizing Continuous Prompts for Generation
Paper • 2101.00190 • Published • 6 -
GPT Understands, Too
Paper • 2103.10385 • Published • 11
Collection by Apr 7, 2024
-
Efficient Few-Shot Learning Without Prompts
Paper • 2209.11055 • Published • 5 -
Parameter-Efficient Transfer Learning for NLP
Paper • 1902.00751 • Published • 2 -
GPT Understands, Too
Paper • 2103.10385 • Published • 11 -
The Power of Scale for Parameter-Efficient Prompt Tuning
Paper • 2104.08691 • Published • 10
Collection by Jun 12, 2024
-
The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits
Paper • 2402.17764 • Published • 628 -
When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method
Paper • 2402.17193 • Published • 26 -
Training-Free Long-Context Scaling of Large Language Models
Paper • 2402.17463 • Published • 24 -
The Power of Scale for Parameter-Efficient Prompt Tuning
Paper • 2104.08691 • Published • 10
Collection by Feb 26, 2024