Papers by Youngjin Song
Lossless Acceleration of Large Language Models with Hierarchical Drafting based on Temporal Locality in Speculative Decoding (2025.findings-naacl)
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Sukmin Cho, Sangjin Choi, Taeho Hwang, Jeongyeon Seo, Soyeong Jeong, Huije Lee, Hoyun Song, Jong C. Park, Youngjin Kwon
| Challenge: | Existing methods for drafting and verifying tokens require significant fine-tuning or have inconsistent performance across tasks. |
| Approach: | They propose a lossless drafting approach that organizes various token sources into multiple databases in a hierarchical framework based on temporal locality. |
| Outcome: | The proposed method outperforms existing database drafting methods on Spec-Bench using 7B and 13B parameters. |
C2: Scalable Auto-Feedback for LLM-based Chart Generation (2025.naacl-long)
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Woosung Koh, Janghan Yoon, MinHyung Lee, Youngjin Song, Jaegwan Cho, Jaehyun Kang, Taehyeon Kim, Se-Young Yun, Youngjae Yu, Bongshin Lee
| Challenge: | generating high-quality charts with Large Language Models presents significant challenges due to limited data and the high cost of curation. |
| Approach: | They propose a referencefree automatic feedback generator to generate high-quality charts with Large Language Models. |
| Outcome: | The proposed framework outperforms baselines and shows that it significantly improves data diversity. |
Claim-Guided Textual Backdoor Attack for Practical Applications (2025.findings-naacl)
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| Challenge: | a novel backdoor attack is based on textual claims to trick models into misbehaving on targeted claims. |
| Approach: | a new backdoor attack is designed to trick models into misbehaving on targeted claims . the code and data will be available at https://github.com/minkyoo9/CGBA . |
| Outcome: | a new backdoor attack exploits the power of textual claims to trick models into misbehaving on claims without affecting their performance on clean data. |