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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 -
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Paper • 1810.04805 • Published • 17 -
On the Opportunities and Risks of Foundation Models
Paper • 2108.07258 • Published -
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
Paper • 2204.07705 • Published • 1
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Collections including paper arxiv:2406.01574
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Instruction Pre-Training: Language Models are Supervised Multitask Learners
Paper • 2406.14491 • Published • 90 -
Transformers are SSMs: Generalized Models and Efficient Algorithms Through Structured State Space Duality
Paper • 2405.21060 • Published • 66 -
Perplexed by Perplexity: Perplexity-Based Data Pruning With Small Reference Models
Paper • 2405.20541 • Published • 24 -
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
Paper • 2406.01574 • Published • 46
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Vript: A Video Is Worth Thousands of Words
Paper • 2406.06040 • Published • 29 -
ShareGPT4Video: Improving Video Understanding and Generation with Better Captions
Paper • 2406.04325 • Published • 74 -
MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark
Paper • 2406.01574 • Published • 46 -
Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis
Paper • 2405.21075 • Published • 24
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BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
Paper • 2211.05100 • Published • 29 -
CsFEVER and CTKFacts: Acquiring Czech data for fact verification
Paper • 2201.11115 • Published -
Training language models to follow instructions with human feedback
Paper • 2203.02155 • Published • 17 -
FinGPT: Large Generative Models for a Small Language
Paper • 2311.05640 • Published • 32