Papers by Jeongmin Ahn
PRIME: Ultra-Low-Rank Principal–Residual Model Merging (2026.findings-acl)
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Seung-Ho Lee, Kyungsu Lee, Bazarvaani Zuchi, Jeongmin Ahn, Insuk Seo, Donghyeon Jeon, Inho Kang, Seung-Hoon Na
| Challenge: | Existing methods for model merging have been limited by task-specific performance and task-related tasks. |
| Approach: | They propose an ultra-low-rank principal-residual model merging framework that decomposes task vector merging into two stages. |
| Outcome: | Experiments on eight natural language processing tasks show that PRIME outperforms existing models while preserving the task-specific capabilities of the original models. |