Papers by Max Hahnbück
Resource-Efficient Anonymization of Textual Data via Knowledge Distillation from Large Language Models (2025.coling-industry)
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| Challenge: | Existing approaches to anonymize textual data from large language models pose privacy risks due to their API-based access. |
| Approach: | They propose a method to distill large language models into smaller encoder-only models via named entity recognition coupled with regular expressions to create a lightweight model capable of effective anonymization. |
| Outcome: | The proposed approach reduces computational overhead while maintaining semantic integrity of data. |