Papers by Shang-Tse Chen
Task Arithmetic can Mitigate Synthetic-to-Real Gap in Automatic Speech Recognition (2024.emnlp-main)
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| Challenge: | Existing methods for speech recognition suffer from the synthetic-to-real gap . existing methods suffer from this distributional shift due to acoustic mismatches . |
| Approach: | They propose to use task arithmetic to fine-tune an ASR model on synthetic data to mitigate the synthetic-to-real gap. |
| Outcome: | The proposed method shows an improvement of 10.03% over baselines on the SLURP dataset. |
Jailbreaking with Universal Multi-Prompts (2025.findings-naacl)
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| Challenge: | Large language models (LLMs) have seen rapid development in recent years, but ethical concerns and new types of attacks have emerged. |
| Approach: | They propose a prompt-based method to jailbreak large language models using universal multi-prompts and an approach for defense that outperforms existing techniques. |
| Outcome: | The proposed method outperforms existing techniques for jailbreaking LLMs using universal multi-prompts. |
Safeguard Fine-Tuned LLMs Through Pre- and Post-Tuning Model Merging (2025.findings-emnlp)
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| Challenge: | Fine-tuning large language models for downstream tasks often leads to catastrophic forgetting, notably degrading the safety of original alignments. |
| Approach: | They propose to merge the weights of pre- and post-fine-tuned models to improve safety while enhancing performance. |
| Outcome: | Experiments across different downstream tasks and models validate the method’s practicality and effectiveness. |
Adaptive Helpfulness–Harmlessness Alignment with Preference Vectors (2026.eacl-long)
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Ren-Wei Liang, Chin Ting Hsu, Chan-Hung Yu, Saransh Agrawal, Shih-Cheng Huang, Chieh-Yen Lin, Shang-Tse Chen, Kuan-Hao Huang, Shao-Hua Sun
| Challenge: | Existing approaches to balancing helpfulness and harmlessness suffer from performance conflicts, limited controllability, and poor extendability. |
| Approach: | They propose a framework that allows users to control their own preferences and dynamically merge them at test time. |
| Outcome: | The proposed framework improves helpfulness without conservatism and smooth control over preference trade-offs. |
Pseudo2Real: Task Arithmetic for Pseudo-Label Correction in Automatic Speech Recognition (2026.findings-acl)
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| Challenge: | ASR models can be used to correct accent-specific errors without ground truth . pseudo-labels inherit the teacher model's systematic biases, authors say . |
| Approach: | They propose a parameter-space correction technique that captures pseudo-label biases . they propose achieving up to 35% relative WER reduction on a pseudo-labeled target model . |
| Outcome: | The proposed model achieves 35% relative WER reduction on ten African accents with the Whisper tiny model. |