Papers by I-Hsin Chung
STAR: Spectral Truncation and Rescale for Model Merging (2025.naacl-short)
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| Challenge: | Model merging is an efficient way of obtaining a multi-task model from several pretrained models without further fine-tuning. |
| Approach: | They propose a model merging technique that aims at mitigating "merging conflicts" by truncating small components in the respective spectral spaces and then an automatic parameter rescaling scheme to retain the nuclear norm of the original matrix. |
| Outcome: | The proposed model outperforms baseline models on flan-T5 by 4.2% and is robust to hyperparamater choice. |
Attention Tracker: Detecting Prompt Injection Attacks in LLMs (2025.findings-naacl)
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| Challenge: | Large Language Models (LLMs) are vulnerable to prompt injection attacks, where malicious inputs manipulate the model into ignoring original instructions and executing designated actions. |
| Approach: | They propose a training-free method that tracks attention patterns on instruction to detect prompt injection attacks without additional inference. |
| Outcome: | The proposed method shows an improvement of up to 10.0% over existing methods and performs well even on small LLMs. |