Improving Causal Interventions in Amnesic Probing with Mean Projection or LEACE
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Abstract (arXiv)
Amnesic probing is a technique used to examine the influence of specific linguistic information on the behaviour of a model. This involves identifying and removing the relevant information and then assessing whether the model's performance on the main task changes. If the removed information is relevant, the model's performance should decline. The difficulty with this approach lies in removing only the target information while leaving other information unchanged. It has been shown that Iterative Nullspace Projection (INLP), a widely used removal technique, introduces random modifications to representations when eliminating target information. We demonstrate that Mean Projection (MP) and LEACE, two proposed alternatives, remove information in a more targeted manner, thereby enhancing the potential for obtaining behavioural explanations through Amnesic Probing.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 8 of its 16 references that are in the map. First posted on arXiv 13 Jun 2025.
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8 of 16 references.
References · 8
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2021Probing Classifiers: Promises, Shortcomings, and Advancescited by 876
- 2020Null It Out: Guarding Protected Attributes by Iterative Nullspace Projectioncited by 455
- 2018Analysis Methods in Neural Language Processing: A Surveycited by 277
- 2023LEACE: Perfect linear concept erasure in closed formcited by 267
- 2021Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyondcited by 178
- 2022Kernelized Concept Erasurecited by 48
- 2022Better Hit the Nail on the Head than Beat around the Bush: Removing Protected Attributes with a Single Projectioncited by 17
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Citing papers per year: 2026: 4.
4 papers.
- 2026The Cascade Equivalence Hypothesis: When Do Speech LLMs Behave Like ASRrightarrowLLM Pipelines?cited by 5
- 2026Iterative Erasure Count Is Not an Affine-Invariant Concept Dimensioncited by 1
- 2026ParaGeo: Decomposing Paralinguistic Variation into a Shared Latent Geometrycited by 0
- 2026Extracting Persona Subspaces Through Iterative Nullspace Projection For Modulationcited by 0
- 2026Extracting Persona Subspaces Through Iterative Nullspace Projection For Modulation3 Oct 2026
- 2026ParaGeo: Decomposing Paralinguistic Variation into a Shared Latent Geometry2 Oct 2026
- 2026Iterative Erasure Count Is Not an Affine-Invariant Concept Dimension11 Aug 2026
- 2026The Cascade Equivalence Hypothesis: When Do Speech LLMs Behave Like ASRrightarrowLLM Pipelines?19 Feb 2026