Papers by Yichen Yu
CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Low Resource With Contrastive Learning (2023.emnlp-main)
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| Challenge: | Recent proposed methods fail to consider the linguistic structure of texts and lack the ability to handle the low-resource problem. |
| Approach: | They propose a coherence-based contrastive learning model named CoCo to detect MGTs under low-resource scenario. |
| Outcome: | The proposed model outperforms state-of-the-art methods on two datasets and two self-constructed datasets. |
INSET: Sentence Infilling with INter-SEntential Transformer (2020.acl-main)
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| Challenge: | Missing sentence generation fosters a wide range of applications in natural language generation . Developing models for sentence infilling can potentially facilitate many text generation applications . |
| Approach: | They propose a framework to decouple the problem from natural language processing . they propose generating missing sentences that can syntactically and semantically bridge context . |
| Outcome: | The proposed model learns a sentence representation and generates 'missing sentences' the proposed model can be used for document auto-completion and meeting note expansion . |
TaoType: Predicting Fine-Grained Typing Intent for Faster Search (2026.acl-industry)
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| Challenge: | Large-scale industrial ranking systems operate under stringent real-time performance requirements. |
| Approach: | They propose a client-side framework that determines whether a user’s query is complete at each typing . this method leverages client-based typing behavior for real-time early prediction . |
| Outcome: | The proposed framework achieves offline precision/recall/accuracy of 0.7936/0.8196/0.7742 and decreases online response time by 640.5193.65 milliseconds. |
WaterSeeker: Pioneering Efficient Detection of Watermarked Segments in Large Documents (2025.findings-naacl)
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| Challenge: | Existing methods focus on distinguishing fully watermarked text from non-watermarked text, overlooking real-world scenarios where LLMs generate only brief segments within longer documents. |
| Approach: | They propose a method to detect watermarked segments in large documents using an anomaly extraction method and a local traversal. |
| Outcome: | The proposed method achieves a superior balance between detection accuracy and computational efficiency. |
Jailbreak Large Vision-Language Models Through Multi-Modal Linkage (2025.acl-long)
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| Challenge: | Existing methods to jailbreak large vision-language models fail against cutting-edge models such as GPT-4o, despite having undergone safety alignment training. |
| Approach: | They propose a new framework for jailbreaking large vision-language models that uses an encryption-decryption process to mitigate the over-exposure of harmful information. |
| Outcome: | The proposed framework jailbreaks GPT-4o with 99.40% success rates on SafeBench, 98.81% on MM-SafeBench and 99.07% on HADES-Dataset. |
SeRTS: Self-Rewarding Tree Search for Biomedical Retrieval-Augmented Generation (2024.findings-emnlp)
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Minda Hu, Licheng Zong, Hongru Wang, Jingyan Zhou, Jingjing Li, Yichen Gao, Kam-Fai Wong, Yu Li, Irwin King
| Challenge: | Existing retrieval-augmented approaches to large language models face performance limitations due to the lack of publicly available training data. |
| Approach: | They propose a plug-and-play LLM-based retrieval method called Self-Rewarding Tree Search based on Monte Carlo Tree Search and a self-rewarding paradigm to address these limitations. |
| Outcome: | The proposed method improves the performance of the BM25 retriever and surpasses the baseline of self-reflection in both efficiency and scalability. |
On the Generation of Medical Dialogs for COVID-19 (2021.acl-short)
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Meng Zhou, Zechen Li, Bowen Tan, Guangtao Zeng, Wenmian Yang, Xuehai He, Zeqian Ju, Subrato Chakravorty, Shu Chen, Xingyi Yang, Yichen Zhang, Qingyang Wu, Zhou Yu, Kun Xu, Eric Xing, Pengtao Xie
| Challenge: | under the pandemic of COVID-19, people experiencing COVI D19-related symptoms have a pressing need to consult doctors. |
| Approach: | They develop a medical dialog system that can provide COVID19-related consultations . they use two dialog datasets containing conversations between doctors and patients . |
| Outcome: | The proposed system can provide COVID19-related consultations, but is too small compared with general-domain dialog datasets. |
Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be Better (2024.acl-long)
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Shengchao Liu, Xiaoming Liu, Yichen Wang, Zehua Cheng, Chengzhengxu Li, Zhaohan Zhang, Yu Lan, Chao Shen
| Challenge: | Existing methods to detect MGT from human-written texts are inadequate . existing methods are fine-tuned and zero-shot metric-based, but they can be more accurate. |
| Approach: | They propose a novel fine-tuned detector that can detect MGT from human-written texts by contrastive learning on selective perturbation. |
| Outcome: | The proposed method outperforms the state-of-the-art by 1.20% on four public datasets. |
FuseSearch: Learning Adaptive Parallel Execution for Efficient Code Localization (2026.findings-acl)
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Ke Xu, Siyang Xiao, Ming Liang, Yichen Yu, Zhixiang Wang, Jingxuan Xu, Dajun Chen, Wei Jiang, Yong Li
| Challenge: | Existing parallel code localization agents suffer from a 34.9% redundant tool invocation rate . specialized localization agent that operate as dedicated search components is needed to achieve high localization accuracy. |
| Approach: | They propose a parallel code localization system that reframes parallel code execution as a quality–efficiency co-optimization problem. |
| Outcome: | The proposed method matches SOTA performance while being 93.6% faster. |