Uncertainty Unveiled: Can Exposure to More In-context Examples Mitigate Uncertainty for Large Language Models?

Abstract

Recent advances in handling long sequences have unlocked new possibilities for long-context in-context learning (ICL). While existing research predominantly focuses on performance gains driven by additional in-context examples, the impact on the trustworthiness of generated responses remains underexplored. This paper addresses this gap by investigating how increased examples influence predictive uncertainty—an essential aspect in trustworthiness. We begin by systematically quantifying uncertainty across different “shot” configurations in ICL, emphasizing the role of example quantity. Through uncertainty decomposition, we introduce a novel perspective on performance enhancement, focusing on epistemic uncertainty (EU). Our results reveal that additional examples reduce total uncertainty in both simple and complex tasks by injecting task-specific knowledge, thereby diminishing EU and enhancing performance. For complex tasks, these advantages emerge only after addressing the increased noise and uncertainty associated with longer inputs. Finally, we investigate the progression of internal confidence across layers, uncovering the underlying mechanisms that drive the reduction in uncertainty.