Papers by Kyuyoung Kim
Personalized Language Models via Privacy-Preserving Evolutionary Model Merging (2025.emnlp-main)
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| Challenge: | Existing methods for personalization in language models lack explicit mechanisms for privacy preservation. |
| Approach: | They propose a Privacy-Preserving Model Merging via Evolutionary Algorithms to optimize utility while minimizing privacy risks. |
| Outcome: | The proposed approach outperforms baseline models on the LaMP benchmark and achieves 45% improvement in task performance. |
Margin Matching Preference Optimization: Enhanced Model Alignment with Granular Feedback (2024.findings-emnlp)
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| Challenge: | Existing methods for large language models rely on binary labels that fail to capture the subtle differences in relative quality between pairs. |
| Approach: | They propose a method that incorporates relative quality margins into optimization to improve LLM policies and reward models. |
| Outcome: | The proposed approach outperforms baseline methods on popular benchmarks including MT-bench and RewardBench. |
Mamba Drafters for Speculative Decoding (2025.findings-emnlp)
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Daewon Choi, Seunghyuk Oh, Saket Dingliwal, Jihoon Tack, Kyuyoung Kim, Woomin Song, Seojin Kim, Insu Han, Jinwoo Shin, Aram Galstyan, Shubham Katiyar, Sravan Babu Bodapati
| Challenge: | Existing drafters that use external drafters suffer from slower drafting while self-speculation methods use drafters tailored to the target model but require re-training. |
| Approach: | They propose a drafter based on a state space model, Mamba, as a solution that combines the best aspects of both approaches. |
| Outcome: | The proposed drafters outperform existing drafters while using less memory and maintaining their cross-model adaptability. |