Se2's sequential example selection approach contributes to enhancing in-context learning by capturing the internal relationships and sequential information among examples. Traditional methods often overlook these aspects, leading to suboptimal performance in downstream tasks. By formulating the problem as a sequential selection issue, Se2 can model the conditional probability of example sequences given varying context inputs. This allows Se2 to better understand the interrelationships between examples and select more relevant and contextual prompts for in-context learning. Additionally, utilizing beam search helps construct diverse and high-quality example sequences, further enriching the contextuality and relevance of ICL prompts.