Potential limitations or biases that could affect the effectiveness of Se2 in real-world applications include inherent biases present within large language models (LLMs) used for feedback. These biases may influence the selected examples and ultimately impact task performance. Additionally, computational resource constraints may limit the scalability of Se2 when dealing with larger datasets or more complex NLP tasks. Furthermore, relying solely on LLM feedback for example selection may introduce model-specific biases that could hinder generalizability across different models or tasks.