  In-context learning (ICL) enables large language models (LLMs) to perform new
tasks by using sample-label pairs as demonstrations. However, variations in
demonstrations can lead to significantly different performances. Current
research mainly focuses on selecting demonstration samples, preassuming the
class name to be the label word when creating sample-label pairs. However, the
choice of label words is crucial for ICL performance. In addition, we observe
that using a single class name in demonstration may not yield optimal results.
In this paper, we propose to use multiple label words in one sample-label pair
to enhance ICL performance. Further, we select and order sample-label pairs
based on LLM's output distribution, aiming to optimize the demonstration
examples from both the samples' and labels' perspectives. Evaluation results on
seven classification datasets show that the use of multiple label words,
strategically organized by their selection, order and quantity, improves ICL
performance through diverse label information.
