Efficient-Empathy：迈向高效且有效的共情数据选择
Efficient-Empathy: Towards Efficient and Effective Selection of Empathy Data
AI总结：
针对大语言模型共情数据训练无质量筛选导致的效率低、性能差问题，提出基于感性与理性评分的Efficient-Empathy数据选择算法，仅用59%的感性数据即达SoTA性能，结合MoE结构效果更优。
摘要：
近年来，随着大语言模型（LLMs）的快速发展，具备出色的共情响应能力已成为一项关键前提。因此，管理和理解大规模视频数据集的重要性日益提升。然而，共情数据通常在未经过任何质量筛选的情况下被用于训练，导致数据利用效率低下、计算资源浪费。此外，使用原始数据还会造成共情对话的性能偏低。在本研究中，我们提出了Efficient-Empathy——一种基于感性与理性评分的数据选择算法，能够自动筛选出感性和理性数据，同时剔除低质量数据。仅使用感性数据（占完整数据集的59%），我们训练的感性模型就高效达到了当前最优（SoTA）性能。此外，在设置多组数据选择超参数的情况下，该感性模型仍能展现出SoTA性能，证明了我们方法的鲁棒性。通过将感性与理性数据与MoE（混合专家）结构相结合，我们取得了更优的性能，进一步验证了Efficient-Empathy算法的有效性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
