Papers by Sangyeon Yu
FractalLLM: Lossless Self-Speculative Decoding with Layer Embedded Self-Compression (2025.findings-emnlp)
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| Challenge: | Autoregressive decoding requires a full forward pass for each generated token, increasing inference latency. |
| Approach: | They propose a lossless self-speculative decoding method that embeds a compressed model within selected decoder layers of the original model. |
| Outcome: | The proposed method achieves substantial speed-ups (up to 2.47) over standard autoregressive decoding. |
DUSK: Do Not Unlearn Shared Knowledge (2026.findings-acl)
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| Challenge: | Recent work suggests that machine learning models are indistinguishable from models trained on retain sets. |
| Approach: | They propose a benchmark to evaluate machine unlearning under realistic knowledge overlap . they construct documents containing both shared and unique knowledge . |
| Outcome: | The proposed model is indistinguishable from a model retrained on the retain set while only forget-specific content is removed. |