Soft-Label Integration for Robust Toxicity Classification
Authors: Zelei Cheng, Xian Wu, Jiahao Yu, Shuo Han, Xin-Qiang Cai, Xinyu Xing
NeurIPS 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our approach outperforms existing baseline methods in terms of both average and worst-group accuracy, confirming its effectiveness in leveraging crowdsourced annotations to achieve more effective and robust toxicity classification. |
| Researcher Affiliation | Academia | Zelei Cheng Northwestern University Evanston, USA EMAIL Xian Wu Northwestern University Evanston, USA EMAIL Jiahao Yu Northwestern University Evanston, USA EMAIL Shuo Han Northwestern University Evanston, USA EMAIL Xin-Qiang Cai The University of Tokyo Tokyo, Japan EMAIL Xinyu Xing Northwestern University Evanston, USA EMAIL |
| Pseudocode | Yes | We provide the full algorithm in Algorithm 1. |
| Open Source Code | Yes | We release the data and code in https://github. com/chengzelei/crowdsource_toxicity_classification. |
| Open Datasets | Yes | Additionally, we conduct our experiments on the public Hate Xplain dataset [20]. |
| Dataset Splits | Yes | For each classification task, we have a large training set with crowdsourced annotations (i.e., 6,941 samples for toxic question classification and 28,194 samples for toxic response classification) and a testing set containing 2,000 samples with ground truth. The validation set with ground truth includes a small number of samples (i.e., 1,000 samples) from the training set. |
| Hardware Specification | Yes | We train the machine learning models on a server with 8 NVIDIA A100 80GB GPUs and 4TB memory for all the learning algorithms. |
| Software Dependencies | Yes | The toxicity classifier and soft-label weight estimator are both implemented based on the transformers library of version 4.34.1 [62]. |
| Experiment Setup | Yes | We list the hyper-parameter settings for all experiments in Appendix C.3. |