Papers by Anna Borisiuk
Anatomy of Unlearning: The Dual Impact of Fact Salience and Model Fine-Tuning (2026.findings-acl)
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| Challenge: | Existing studies assume that all facts are equally forgettable . popular facts, frequent and widely distributed, may be more deeply embedded than rare ones, making them harder to erase. |
| Approach: | They propose a benchmark to evaluate how unlearning differs between pretrained and supervised fine-tuned models when fact popularity is taken into account. |
| Outcome: | The proposed model is compared with pretrained and SFT models on the forget data and shows that it performs better on both models. |
The Silence of the Facts: Popularity as a Barrier to Machine Unlearning (2026.acl-srw)
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| Challenge: | Existing unlearning methods assume that all facts are equally challenging to forget . large models struggle more to forget popular entities, damaging related knowledge in the process . |
| Approach: | They build a benchmark to investigate whether fact popularity influences the efficiency of LLM unlearning. |
| Outcome: | The proposed benchmark compares state-of-the-art models on a set of models of different sizes. |