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In-context learning proves competitive with LLM fine-tuning when data is scarce

Retrieved on: 2024-10-20 15:44:05

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In-context learning proves competitive with LLM fine-tuning when data is scarce. View article details on hiswai:

Summary

The article discusses a study by EPFL that compares in-context learning (ICL) and instruction fine-tuning (IFT) for adapting large language models. The study finds ICL effective with limited data, but IFT excels in complex tasks, underscoring the key role of high-quality data. This relates to key NLP concepts and tags like deep learning, large language models, and fine-tuning.

Article found on: the-decoder.com

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