Synth-Empathy: Towards High-Quality Synthetic Empathy Data
<|reference_start|>Synth-Empathy: Towards High-Quality Synthetic Empathy Data: 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.<|reference_end|>
@article{liang2024synth-empathy:,
    title={Synth-Empathy: Towards High-Quality Synthetic Empathy Data},
    author={Hao Liang, Linzhuang Sun, Jingxuan Wei, Xijie Huang, Linkun Sun, Bihui
  Yu, Conghui He, Wentao Zhang},
    journal={arXiv preprint arXiv:2407.21669},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.21669},
    primaryClass={cs.CL cs.LG}
}