Fine-tuning vs. in-context learning: New research guides better LLM customization for real-world tasks

Fine-tuning vs. in-context learning: New research guides better LLM customization for real-world tasks

Source: Venture Beat Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Two popular approaches for customizing large language models (LLMs) for downstream tasks are fine-tuning and in-context learning (ICL). In a recent study, researchers at Google DeepMind and Stanford University explored the generalization capabilities…

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