Papers by Shanghaoran Quan
English as Defense Proxy: Mitigating Multilingual Jailbreak via Eliciting English Safety Knowledge (2025.findings-emnlp)
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| Challenge: | Large language models excel in many tasks, but their safety guarantees vary by language. |
| Approach: | They propose a unified approach that leverages English as a universal safety anchor. |
| Outcome: | The proposed approach leverages English as defense proxy (E-Proxy) to transfer safety knowledge across languages. |
Qwen2.5-xCoder: Multi-Agent Collaboration for Multilingual Code Instruction Tuning (2025.acl-long)
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Jian Yang, Wei Zhang, Yibo Miao, Shanghaoran Quan, Zhenhe Wu, Qiyao Peng, Liqun Yang, Tianyu Liu, Zeyu Cui, Binyuan Hui, Junyang Lin
| Challenge: | Existing methods to train code LLMs view each programming language in isolation . experimental results show that Qwen2.5-xCoder can bridge the gap between different programming languages . |
| Approach: | They propose a framework that allows agents to collaborate to enhance multilingual instruction tuning for code LLMs. |
| Outcome: | Experimental results show that Qwen2.5-xCoder can transfer knowledge efficiently and effectively between languages. |
DMoERM: Recipes of Mixture-of-Experts for Effective Reward Modeling (2024.findings-acl)
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| Challenge: | Using a reward model (RM) to improve the effectiveness of large language models, there are two challenges in training. |
| Approach: | They propose a reward model (RM) that is a proxy of human preferences and assigns scores to the outputs of the large language model (LLM) a human annotation consistency rate of 60% to 75% is causing training data to contain a lot of noise. |
| Outcome: | The proposed model outperforms state-of-the-art ensemble methods and mitigates the overoptimization problem. |