Summary of What Happens to a Dataset Transformed by a Projection-based Concept Removal Method?, By Richard Johansson
What Happens to a Dataset Transformed by a Projection-based Concept Removal Method?
by Richard Johansson
First submitted to arxiv on: 24 Mar 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 This paper explores methods that remove information about a concept from language representations using linear projections. The authors investigate what happens when these transformed datasets are applied to different tasks and benchmarks. Through theoretical analysis and experiments on real-world and synthetic data, the study shows that these methods introduce strong statistical dependencies into the transformed datasets. The representation space becomes highly structured, allowing for the reconstruction of original labeling by applying an anti-clustering method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at ways to remove information about a concept from language representations using linear projections. They want to know what happens when they apply this transformation to real-world data and synthetic data. They found that these methods create strong patterns in the transformed data, which can be used to reconstruct the original labels. |
Keywords
» Artificial intelligence » Clustering » Synthetic data
