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.

本研究解决了同理心数据不足和人工标签浪费的问题，提出了一种基于大型语言模型的合成同理心数据生成和筛选方法Synth-Empathy。这一方法有助于自动生成高质量同理心数据，进一步提升同理心响应性能，且在多项评估基准上达到最新的优越表现，展示了其在实际应用中的有效性和鲁棒性。

合成同理心：迈向高质量合成同理心数据