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AI Summary
- Jeremy Howard from Fast.ai discusses how finetuning paradigms are shifting with larger context windows and better base models reducing the need for traditional finetuning
- ULMFiT pioneered transfer learning for NLP in 2018, demonstrating that pre-trained models could achieve SOTA with small task-specific datasets via three-step finetuning
- Modern LLMs with expanded context and instruction-tuning capabilities are potentially making specialized finetuning less necessary, marking an era transition in AI development