Is In-Context Learning Sufficient for Instruction Following in LLMs?

Understand

In-context learning (ICL) allows LLMs to learn from examples without changing their weights: this is a particularly promising capability for long-context LLMs that can potentially learn from many examples.

• Recently, Lin et al.
• (2024) proposed URIAL, a method using only three in-context examples to align base LLMs, achieving non-trivial instruction following performance.
• In this work, we show that, while effective, ICL alignment with URIAL still underperforms compared to instruction fine-tuning on the established benchmark MT-Bench, especially with more capable base LLMs.