Optimal-Demo-Selection-ICL
PublicImplements and benchmarks optimal demonstration selection strategies for In-Context Learning (ICL) using LLMs. Covers IDS, RDES, Influence-based Selection, Se², and TopK+ConE across reasoning and classification tasks, analyzing the impact of example relevance, diversity, and ordering on model performance across multiple architectures.