Papers by Ruofan Wang
A Mousetrap: Fooling Large Reasoning Models for Jailbreak with Chain of Iterative Chaos (2025.findings-acl)
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| Challenge: | Large Reasoning Models (LRMs) have advanced beyond traditional Large Language Models, yet they pose heightened safety risks. |
| Approach: | They propose a first jailbreak attack targeting Large Reasoning Models . they exploit a Chaos Machine component to transform attack prompts with diverse one-to-one mappings based on the reasoning chain . |
| Outcome: | The proposed attack exploits the unique vulnerabilities of LRMs by integrating a Chaos Machine. success rates of the mousetrap attack are as high as 96%, 86% and 98% respectively. |
Chain-of-Scrutiny: Detecting Backdoor Attacks for Large Language Models (2025.findings-acl)
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| Challenge: | Large Language Models (LLMs) have demonstrated impressive capabilities across various domains, but are vulnerable to backdoor attacks. |
| Approach: | They propose a chain-of-scrutiny approach which leverages LLMs’ unique reasoning abilities to mitigate backdoor attacks. |
| Outcome: | The proposed model is well-suited for the popular API-only LLM deployments, enabling detection at minimal cost and with little data. |
A Survey of Pun Generation: Datasets, Evaluations and Methodologies (2025.findings-emnlp)
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| Challenge: | Pun generation aims to modify linguistic elements in text to produce humour or evoke double meanings. |
| Approach: | They propose to review pun generation datasets and methods across different stages . pun generation aims to produce humour or evoke double meanings . |
| Outcome: | This paper summarises both automated and human evaluation metrics used to assess the quality of pun generation. |
D2GCLF: Document-to-Graph Classifier for Legal Document Classification (2022.findings-naacl)
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| Challenge: | Existing methods learn latent representations for each document by considering the semantics and themes of the documents. |
| Approach: | They propose a document-to-graph classifier which extracts facts as relations between key participants in a law case and represents a legal document with four relation graphs. |
| Outcome: | The proposed method outperforms the state-of-the-art methods on a real-world legal document dataset. |
Prompt-Singer: Controllable Singing-Voice-Synthesis with Natural Language Prompt (2024.naacl-long)
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Yongqi Wang, Ruofan Hu, Rongjie Huang, Zhiqing Hong, Ruiqi Li, Wenrui Liu, Fuming You, Tao Jin, Zhou Zhao
| Challenge: | Recent singing-voice-synthesis methods lack ability to control style attributes of synthesized singing. |
| Approach: | They propose a singing-voice-synthesis method that enables attribute controlling on singer gender, vocal range and volume with natural language. |
| Outcome: | The proposed method achieves favorable control ability and audio quality. |
SKGSum: Structured Knowledge-Guided Document Summarization (2024.findings-acl)
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Qiqi Wang, Ruofan Wang, Kaiqi Zhao, Robert Amor, Benjamin Liu, Jiamou Liu, Xianda Zheng, Zijian Huang
| Challenge: | Existing summarization methods ignore the importance of summary structure, resulting in summaries that emphasize the most prominent information while omitting essential details from other sections. |
| Approach: | They propose a method that uses automatically extracted summary points to generate summaries. |
| Outcome: | The proposed methods improve quality and BERTScore of summaries and broaden the types of documents that can be effectively summarized. |