  In recent years, with the rapid advancements in large language models (LLMs),
achieving excellent empathetic response capabilities has become a crucial
prerequisite. Consequently, managing and understanding empathetic datasets have
gained increasing significance. However, empathetic data are typically
human-labeled, leading to insufficient datasets and wasted human labor. In this
work, we present Synth-Empathy, an LLM-based data generation and quality and
diversity selection pipeline that automatically generates high-quality
empathetic data while discarding low-quality data. With the data generated from
a low empathetic model, we are able to further improve empathetic response
performance and achieve state-of-the-art (SoTA) results across multiple
benchmarks. Moreover, our model achieves SoTA performance on various human
evaluation benchmarks, demonstrating its effectiveness and robustness in
real-world applications. Furthermore, we show the trade-off between data
quantity and quality, providing insights into empathetic data generation and
selection.
