Papers with Iterative Nullspace Projection

2 papers
An Empirical Survey of the Effectiveness of Debiasing Techniques for Pre-trained Language Models (2022.acl-long)

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Challenge: Recent work has shown pre-trained language models capture social biases from the large amounts of text they are trained on.
Approach: They propose to use Counterfactual Data Augmentation, Dropout, Iterative Nullspace Projection, Self-Debias, and SentenceDebia as bias mitigation techniques to quantify their effectiveness.
Outcome: The proposed techniques are Counterfactual Data Augmentation (CDA), Dropout, Iterative Nullspace Projection, Self-Debias, and SentenceDebia.
Improving Causal Interventions in Amnesic Probing with Mean Projection or LEACE (2025.findings-acl)

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Challenge: Amnesic probing examines the influence of specific linguistic information on the behaviour of a model by identifying and removing a property and then testing whether the model can still perform well on next-word prediction.
Approach: They propose to use iterative nullspace projection to remove information by iterating on the target property and then assessing whether the model's performance changes.
Outcome: The proposed methods remove information in a more targeted manner, thereby enhancing the potential for obtaining behavioural explanations through Amnesic Probing.

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