Papers by Daehyun Kim
Contrastive Learning as a Polarizer: Mitigating Gender Bias by Fair and Biased sentences (2024.findings-naacl)
Copied to clipboard
| Challenge: | Recent studies have highlighted social biases inherent in training data can lead models to learn and propagate them. |
| Approach: | They propose a contrastive learning method that uses anchor points to push further negatives and pull closer positives within the representation space. |
| Outcome: | The proposed method achieves state-of-the-art in the ICAT score on the StereoSet, a benchmark for measuring bias in models. |