REVS: Unlearning Sensitive Information in Language Models via Rank Editing in the Vocabulary Space (2025.findings-acl)
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| Challenge: | Current approaches to address this issue involve costly dataset scrubbing or model filtering through unlearning and model editing. |
| Approach: | They propose a method for unlearning sensitive information from language models . they curate email and URL datasets and a social security number dataset . |
| Outcome: | The proposed method shows superior performance and robustness to extraction attacks on real-world datasets. |
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| Challenge: | Recent work shows that an adversary can extract training data from Pretrained Language Models including Personally Identifiable Information (PII) such as names, phone numbers, and email addresses. |
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Unintended Memorization of Sensitive Information in Fine-Tuned Language Models (2026.eacl-long)
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Marton Szep, Jorge Marin Ruiz, Georgios Kaissis, Paulina Seidl, Rüdiger von Eisenhart-Rothe, Florian Hinterwimmer, Daniel Rueckert
| Challenge: | Large Language Models (LLMs) on sensitive datasets carry a substantial risk of unintended memorization and leakage of Personally Identifiable Information (PII) prior studies have analyzed memorizing dynamics in LLMs during pre-training and fine-tuning. |
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DEPN: Detecting and Editing Privacy Neurons in Pretrained Language Models (2023.emnlp-main)
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| Challenge: | Using data sanitization methods to remove personal information from spoken messages is not effective because privacy-transformed data is unlikely to match the test distribution. |
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| Challenge: | Large language models face vulnerabilities related to the extraction of sensitive information. |
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R.R.: Unveiling LLM Training Privacy through Recollection and Ranking (2025.findings-acl)
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| Challenge: | Existing privacy attacks focus on membership inference or data extraction, but reconstructing specific personally identifiable information (PII) in training data remains challenging. |
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PATS: Sensitivity-aware Noisy Learning for Pretrained Language Models (2022.emnlp-main)
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| Challenge: | A wide range of NLP tasks benefit from fine-tuning of pretrained language models (PLMs), however, a number of redundant parameters which contribute less to the downstream task are observed in a directly fine- tuned model. |
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| Challenge: | Existing defenses for large language models do not account for the sequential nature of text data. |
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| Challenge: | Large Language models (LLMs) are trained on vast amounts of data, including sensitive information that poses a risk to personal privacy if exposed. |
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Fine-grained Pluggable Gradient Ascent for Knowledge Unlearning in Language Models (2024.emnlp-main)
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| Challenge: | Existing studies on knowledge unlearning focus on computer vision but extend their exploration to other fields. |
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