Summary of Unlabeled Debiasing in Downstream Tasks Via Class-wise Low Variance Regularization, by Shahed Masoudian et al.
Unlabeled Debiasing in Downstream Tasks via Class-wise Low Variance Regularization
by Shahed Masoudian, Markus Frohmann, Navid Rekabsaz, Markus Schedl
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 A novel debiasing regularization technique is introduced to mitigate biases in pre-trained language models during fine-tuning for downstream tasks. The approach, based on class-wise variance of embeddings, does not require protected attribute labels and can target any attribute, addressing limitations of existing methods that rely on gender-specific words or require labeled data. Experimental results demonstrate the technique outperforms strong debiasing baselines while maintaining performance on the target task. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A language model is a computer program that can understand and generate human-like text. The problem with these models is that they often learn biases from the data they were trained on, like how people are treated unfairly because of their gender or race. To fix this, researchers have come up with many ways to make sure the model doesn’t pick up on these biases in the first place. But even when they do, there’s a problem: if you fine-tune the model for a specific task, it can start picking up those biases again. To solve this, scientists developed a new method that helps keep language models from being biased. This method works by looking at how words are used in different groups and making sure the model doesn’t learn patterns that might be unfair. |
Keywords
» Artificial intelligence » Fine tuning » Language model » Regularization
