Papers by Taesup Moon
PRISP: Privacy-Safe Few-Shot Personalization via Lightweight Adaptation (2026.acl-long)
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| Challenge: | Existing methods for large language model personalization are limited by data-rich settings and privacy risks. |
| Approach: | They propose a lightweight and privacy-safe personalization framework tailored to constraints in large language models. |
| Outcome: | Experiments on a few-shot variant of the LaMP benchmark show that PRISP achieves strong overall performance compared to prior approaches. |
DoMIX: An Efficient Framework for Exploiting Domain Knowledge in Fine-Tuning (2025.acl-long)
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| Challenge: | Existing methods for domain-adaptive pre-training (DAP) face several limitations: high computational cost and GPU memory usage during training; and lack of generalized model for all end tasks. |
| Approach: | They propose a domain-adaptive pre-training (DAP) method that uses a representative parameter-efficient fine-tuning method to provide pre-trained models for specific tasks. |
| Outcome: | The proposed method can be extended beyond the DAP setting to standard LLM fine-tuning scenarios. |