Papers by Dongjin Park
Powerformer: Efficient and High-Accuracy Privacy-Preserving Language Model with Homomorphic Encryption (2025.acl-long)
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| Challenge: | a new privacy-preserving language model, Powerformer, is designed to reduce computation overhead while maintaining model performance. |
| Approach: | They propose an efficient homomorphic encryption-based privacy-preserving language model . it incorporates three key techniques to optimize encrypted computations . |
| Outcome: | The proposed model achieves 45% reduction in computation time compared to state-of-the-art models . authors say the model preserves data privacy and AI capabilities in MLaaS environments . |
One Missing Piece for Open-Source Reasoning Models: A Dataset to Mitigate Cold-Starting Short CoT LLMs in RL (2025.acl-industry)
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Hyungjoo Chae, Dongjin Kang, Jihyuk Kim, Beong-woo Kwak, Sunghyun Park, Haeju Park, Jinyoung Yeo, Moontae Lee, Kyungjae Lee
| Challenge: | Existing large reasoning models are limited by their closed nature and high API costs and safety issues. |
| Approach: | They propose to build a long CoT dataset with existing short CoT LLMs that are not trained for inference-time scaling. |
| Outcome: | The proposed model achieves quality comparable to—or slightly below—R1 and is able to think longer and provide control over the thought budget to better manage the overthinking problem. |