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Fine-tuning vs. in-context learning: New research guides better LLM customization for real-world tasks
By combining fine-tuning and in-context learning, you get LLMs that can learn tasks that would be too difficult or expensive for either method
They constructed “controlled synthetic datasets of factual knowledge” with complex, self-consistent structures, like imaginary family trees or hierarchies of fictional concepts. To ensure they were testing the model’s ability to learn new information, they replaced all nouns, adjectives, and verbs with nonsense terms, avoiding any overlap with the data the LLMs might have encountered during pre-training. This can lead to more robust and reliable LLM applications that perform better on diverse, real-world inputs without incurring the continuous inference-time costs associated with large in-context prompts.
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