Summary of Soft-label Integration For Robust Toxicity Classification, by Zelei Cheng et al.
Soft-Label Integration for Robust Toxicity Classification
by Zelei Cheng, Xian Wu, Jiahao Yu, Shuo Han, Xin-Qiang Cai, Xinyu Xing
First submitted to arxiv on: 18 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (cs.LG)
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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 This paper addresses the issue of classifying toxicity in textual content by introducing a novel bi-level optimization framework. The current approach using empirical risk minimization (ERM) may fail due to exploiting spurious correlations, leading to poor performance on out-of-distribution (OOD) risk. To address this, the authors integrate crowdsourced annotations with soft-labeling and optimize soft-label weights using Group Distributionally Robust Optimization (GroupDRO). This framework enhances robustness against OOD risk. Theoretical convergence is proven, and experimental results show that it outperforms existing methods in terms of average and worst-group accuracy, demonstrating its effectiveness in leveraging crowdsourced annotations for toxicity classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to identify toxic content online by using a new way of training computers. Normally, we train machines to spot bad language by giving them lots of examples from one person’s perspective. But this can be limited and not very good at capturing all the different ways people might think or feel about certain words. To fix this, the researchers used lots of labels from many people and a special technique called soft-labeling. They also added another trick to make sure the machine didn’t just learn to recognize patterns that aren’t really important. The results show that this new approach works much better than previous methods at identifying toxic language. |
Keywords
* Artificial intelligence * Classification * Optimization
