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