What Happens to a Dataset Transformed by a Projection-based Concept Removal Method?
<|reference_start|>What Happens to a Dataset Transformed by a Projection-based Concept Removal Method?: We investigate the behavior of methods that use linear projections to remove information about a concept from a language representation, and we consider the question of what happens to a dataset transformed by such a method. A theoretical analysis and experiments on real-world and synthetic data show that these methods inject strong statistical dependencies into the transformed datasets. After applying such a method, the representation space is highly structured: in the transformed space, an instance tends to be located near instances of the opposite label. As a consequence, the original labeling can in some cases be reconstructed by applying an anti-clustering method.<|reference_end|>
@article{johansson2024what,
    title={What Happens to a Dataset Transformed by a Projection-based Concept
  Removal Method?},
    author={Richard Johansson},
    journal={arXiv preprint arXiv:2403.16142},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.16142},
    primaryClass={cs.CL cs.AI}
}