Summary of Efficient-empathy: Towards Efficient and Effective Selection Of Empathy Data, by Linzhuang Sun et al.
Efficient-Empathy: Towards Efficient and Effective Selection of Empathy Data
by Linzhuang Sun, Hao Liang, Jingxuan Wei, Linkun Sun, Bihui Yu, Bin Cui, Wentao Zhang
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes Efficient-Empathy, an algorithm for selecting high-quality empathetic response capability data from large-scale video datasets. Empathetic data are typically trained without quality selection, leading to inefficiencies and wasted resources. The proposed method uses sensibility and rationality scores to automatically select relevant data while discarding low-quality data. With a sensibility model trained on 59% of the full dataset, the authors achieve state-of-the-art performance. The algorithm’s robustness is demonstrated with multiple hyperparameters. By integrating sensibility and rationality data with a MoE structure, even higher performance is achieved. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how we can improve computers’ ability to understand human emotions by choosing the right data from large video collections. Right now, we’re wasting resources because we’re training these models without selecting the best data. The authors propose a new way to pick the best data based on how well it shows empathy and rationality. They tested their method and found that it works really well, even when using only 59% of the full dataset. By combining different types of data, they were able to get even better results. |
