  In recent years, with the rapid advancements in large language models (LLMs),
achieving excellent empathetic response capability has become a crucial
prerequisite. Consequently, managing and understanding large-scale video
datasets has gained increasing importance. However, empathetic data are
typically trained without any quality selection, leading to inefficient data
usage and wasted computational resources. Additionally, using raw data can
result in low performance in empathetic dialogues. In this work, we present
Efficient-Empathy, a sensibility and rationality score-based data selection
algorithm that automatically selects sensibility and rationality data while
discarding low-quality data. With only the sensibility data (59% of the full
dataset), our trained sensibility model efficiently achieves state-of-the-art
(SoTA) performance. Furthermore, with multiple data selection hyperparameters,
the sensibility model demonstrates SoTA performance, showcasing the robustness
of our method. By integrating sensibility and rationality data with a MoE
structure, we achieve even higher performance, demonstrating the effectiveness
of our Efficient-Empathy algorithm.
