Research reported in \(\texttt{Se}^2\): \(\textit{Se}\)quential Example \(\textit{Se}\)lection for In-Context Learning introduces a new method that considers the sequential nature of example selection for LLM in-context learning. Traditional approaches often ignore the relationships between examples, but \(\texttt{Se}^2\) brings a fresh perspective.

• Proposes a sequential approach to choose examples that build upon each other for LLM training.
• Employs LLM feedback to incorporate the inter-relationships and sequential data knowledge.
• Utilizes algorithms like beam search to explore high-quality example sequences.
• Enhances contextuality and relevance of the examples used for NLP tasks.
• Proved to significantly succeed over standard baselines in various NLP tasks.

\(\texttt{Se}^2\) offers a promising strategy for more effective in-context learning, showcasing the importance of considering the sequence of examples in training LLMs to boost their performance in NLP tasks.