In-context Learning vs. Instruction Tuning: The Case of Small and Multilingual Language Models

The paper investigates whether in‑context learning (ICL) can replace instruction tuning for multilingual language models, especially as model size varies. It highlights the difficulty of obtaining high‑quality instruction data in multilingual settings and compares the performance of ICL versus instruction‑tuned models. The findings show that a performance gap persists between the two approaches, suggesting the need for further research to close it.

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• llms
• fine-tuning
• safety