Papers by Hyung Il Koo
State-offset Tuning: State-based Parameter-Efficient Fine-Tuning for State Space Models (2025.acl-short)
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| Challenge: | State Space Models (SSMs) have emerged as efficient alternatives to Transformers, but their application to SSMs remains unexplored. |
| Approach: | They propose a state-based PEFT method that adjusts state directly instead of using external prompts. |
| Outcome: | The proposed method is based on state-offset tuning, which directly affects state at every timestep. |
TABED: Test-Time Adaptive Ensemble Drafting for Robust Speculative Decoding in LVLMs (2026.findings-eacl)
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Minjae Lee, Wonjun Kang, Byeongkeun Ahn, Christian Classen, Kevin Galim, Seunghyuk Oh, Minghao Yan, Hyung Il Koo, Kangwook Lee
| Challenge: | Large Vision Language Models (LVLMs) are advanced models that process multiple modalities, such as images, audio, and video, alongside text. |
| Approach: | They propose to use a method to generate and verify draft tokens in parallel . they compare existing methods with small draft models and observe performance fluctuations . |
| Outcome: | The proposed method achieves an average walltime speedup of 1.74 over autoregressive decoding and a 5% improvement over single drafting methods. |