Abstract
We investigate the behavior of methods using 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.- Anthology ID:
- 2024.lrec-main.1520
- Volume:
- Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
- Month:
- May
- Year:
- 2024
- Address:
- Torino, Italia
- Editors:
- Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
- Venues:
- LREC | COLING
- SIG:
- Publisher:
- ELRA and ICCL
- Note:
- Pages:
- 17486–17492
- Language:
- URL:
- https://aclanthology.org/2024.lrec-main.1520
- DOI:
- Cite (ACL):
- Richard Johansson. 2024. What Happens to a Dataset Transformed by a Projection-based Concept Removal Method?. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 17486–17492, Torino, Italia. ELRA and ICCL.
- Cite (Informal):
- What Happens to a Dataset Transformed by a Projection-based Concept Removal Method? (Johansson, LREC-COLING 2024)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2024.lrec-main.1520.pdf