Disentangling the Linguistic Competence of Privacy-Preserving BERT

Stefan Arnold; Nils Kemmerzell; Annika Schreiner

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arXiv · 2310.11363

Disentangling the Linguistic Competence of Privacy-Preserving BERT

Stefan Arnold·Nils Kemmerzell·Annika Schreiner

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Abstract

Differential Privacy (DP) has been tailored to address the unique challenges of text-to-text privatization. However, text-to-text privatization is known for degrading the performance of language models when trained on perturbed text. Employing a series of interpretation techniques on the internal representations extracted from BERT trained on perturbed pre-text, we intend to disentangle at the linguistic level the distortion induced by differential privacy. Experimental results from a representational similarity analysis indicate that the overall similarity of internal representations is substantially reduced. Using probing tasks to unpack this dissimilarity, we find evidence that text-to-text privatization affects the linguistic competence across several formalisms, encoding localized properties of words while falling short at encoding the contextual relationships between spans of words.

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Stefan Arnold, Nils Kemmerzell, Annika Schreiner. 2023-10-17. Disentangling the Linguistic Competence of Privacy-Preserving BERT. https://arxiv.org/abs/2310.11363

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