Self-Demos: Eliciting Out-of-Demonstration Generalizability in Large Language Models

Understand

Large language models (LLMs) have shown promising abilities of in-context learning (ICL), adapting swiftly to new tasks with only few-shot demonstrations.

• However, current few-shot methods heavily depend on high-quality, query-specific demos, which are often lacking.
• When faced with out-of-demonstration (OOD) queries, methods that rely on hand-crafted demos or external retrievers might fail.
• To bridge the gap between limited demos and OOD queries, we propose Self-Demos, a novel prompting method that elicits the inherent generalizability in LLMs by query-aware demo generation.