Efficient-Empathy: Towards Efficient and Effective Selection of Empathy Data
This work addresses the challenge of selecting high-quality empathy data for large language models, which is an incremental improvement aimed at researchers and practitioners in natural language processing and AI dialogue systems.
The paper tackles the problem of inefficient data usage and low performance in empathetic dialogues by proposing Efficient-Empathy, a data selection algorithm that uses sensibility and rationality scores to filter low-quality data; with only 59% of the full dataset, the trained sensibility model achieves state-of-the-art performance, and integration with rationality data further improves results.
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.