Fine-tuning vs. in-context learning: New research guides better LLM customization for real-world tasks
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Summary
Recent research from Google DeepMind and Stanford University has compared the generalisation capabilities of large language models (LLMs) that have been fine-tuned and those that have undergone in-context learning (ICL).
While ICL proved more successful in generalisation tasks, it requires more computing power during inference.
Additionally, the researchers proposed a new approach to improve the generalisation capabilities of fine-tuned LLMs by augmenting the learning data using ICL to generate more richly inferred examples.
The augmented data can then be used to fine-tune the model, improving its performance on generalisation tasks.
The researchers suggested the hybrid approach could lead to more robust and reliable LLM applications, although they admitted it makes the model-tuning process more expensive.