  We investigate the role of various demonstration components in the in-context
learning (ICL) performance of large language models (LLMs). Specifically, we
explore the impacts of ground-truth labels, input distribution, and
complementary explanations, particularly when these are altered or perturbed.
We build on previous work, which offers mixed findings on how these elements
influence ICL. To probe these questions, we employ explainable NLP (XNLP)
methods and utilize saliency maps of contrastive demonstrations for both
qualitative and quantitative analysis. Our findings reveal that flipping
ground-truth labels significantly affects the saliency, though it's more
noticeable in larger LLMs. Our analysis of the input distribution at a granular
level reveals that changing sentiment-indicative terms in a sentiment analysis
task to neutral ones does not have as substantial an impact as altering
ground-truth labels. Finally, we find that the effectiveness of complementary
explanations in boosting ICL performance is task-dependent, with limited
benefits seen in sentiment analysis tasks compared to symbolic reasoning tasks.
These insights are critical for understanding the functionality of LLMs and
guiding the development of effective demonstrations, which is increasingly
relevant in light of the growing use of LLMs in applications such as ChatGPT.
Our research code is publicly available at https://github.com/paihengxu/XICL.
