publications

2023

1. TRACE: A Comprehensive Benchmark for Continual Learning in Large Language Models

   Xiao Wang, Yuansen Zhang, Tianze Chen, Songyang Gao, Senjie Jin, Xianjun Yang, and 6 more authors

   In , 2023
2. Shadow Alignment: The Ease of Subverting Safely-Aligned Language Models

   Xianjun Yang, Xiao Wang, Qi Zhang, Linda Petzold, William Yang Wang, Xun Zhao, and 1 more author

   In , 2023
3. Zero-Shot Detection of Machine-Generated Codes

   Xianjun Yang, Kexun Zhang, Haifeng Chen, Linda Petzold, William Yang Wang, and Wei Cheng

   arXiv preprint arXiv:2310.05103, 2023
4. Large Language Models Can Be Good Privacy Protection Learners

   Yijia Xiao, Yiqiao Jin, Yushi Bai, Yue Wu, Xianjun Yang, Xiao Luo, and 5 more authors

   arXiv preprint arXiv:2310.02469, 2023
5. DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text

   Xianjun Yang, Wei Cheng, Linda Petzold, William Yang Wang, and Haifeng Chen

   arXiv preprint arXiv:2305.17359, 2023
6. Enhancing Small Medical Learners with Privacy-preserving Contextual Prompting

   Xinlu Zhang, Shiyang Li, Xianjun Yang, Chenxin Tian, Yao Qin, and Linda Ruth Petzold

   arXiv preprint arXiv:2305.12723, 2023
7. LLMScore: Unveiling the Power of Large Language Models in Text-to-Image Synthesis Evaluation

   Yujie Lu, Xianjun Yang, Xiujun Li, Xin Eric Wang, and William Yang Wang

   arXiv preprint arXiv:2305.11116, 2023
8. Dynamic Prompting: A Unified Framework for Prompt Tuning

   Xianjun Yang, Wei Cheng, Xujiang Zhao, Linda Petzold, and Haifeng Chen

   arXiv preprint arXiv:2303.02909, 2023
9. Exploring the limits of chatgpt for query or aspect-based text summarization

   Xianjun Yang, Yan Li, Xinlu Zhang, Haifeng Chen, and Wei Cheng

   arXiv preprint arXiv:2302.08081, 2023
10. MatKB: Semantic Search for Polycrystalline Materials Synthesis Procedures

    Xianjun Yang, Stephen Wilson, and Linda Petzold

    arXiv preprint arXiv:2302.05597, 2023
11. ReDi: Efficient Learning-Free Diffusion Inference via Trajectory Retrieval

    Kexun Zhang, Xianjun Yang, William Yang Wang, and Lei Li

    arXiv preprint arXiv:2302.02285, 2023
12. OASum: Large-Scale Open Domain Aspect-based Summarization

    Xianjun Yang, Kaiqiang Song, Sangwoo Cho, Xiaoyang Wang, Xiaoman Pan, Linda Petzold, and 1 more author

    In Findings of the Association for Computational Linguistics: ACL 2023, Jul 2023

    Abs

    Aspect or query-based summarization has recently caught more attention, as it can generate differentiated summaries based on users’ interests. However, the current dataset for aspect or query-based summarization either focuses on specific domains, on a relatively small scale, or contains only a few aspect types. Such limitations hinder further explorations in this direction. In this work, we take advantage of crowd-sourcing knowledge on Wikipedia and automatically create a high-quality, large-scale open-domain aspect-based summarization dataset named OASum, which contains more than 3.7 million instances with around 1 million different aspects on 2 million Wikipedia pages. We provide benchmark results on OASum and demonstrate its ability for diverse aspect-based summarization generation. To overcome the data scarcity problem on specific domains, we also perform zero-shot, few-shot, and fine-tuning on seven downstream datasets. Specifically, zero/few-shot and fine-tuning results show that the model pre-trained on our corpus demonstrates a strong aspect or query-focused generation ability compared with the backbone model. Our dataset and pre-trained checkpoints are publicly available.

13. Few-Shot Document-Level Event Argument Extraction

    Xianjun Yang, Yujie Lu, and Linda Petzold

    In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Jul 2023

    Abs

    Event argument extraction (EAE) has been well studied at the sentence level but under-explored at the document level. In this paper, we study to capture event arguments that actually spread across sentences in documents. Prior works usually assume full access to rich document supervision, ignoring the fact that the available argument annotation is limited in production. To fill this gap, we present FewDocAE, a Few-Shot Document-Level Event Argument Extraction benchmark, based on the existing document-level event extraction dataset. We first define the new problem and reconstruct the corpus by a novel N-Way-D-Doc sampling instead of the traditional N-Way-K-Shot strategy. Then we adjust the current document-level neural models into the few-shot setting to provide baseline results under in- and cross-domain settings. Since the argument extraction depends on the context from multiple sentences and the learning process is limited to very few examples, we find this novel task to be very challenging with substantively low performance. Considering FewDocAE is closely related to practical use under low-resource regimes, we hope this benchmark encourages more research in this direction. Our data and codes will be available online.

2022

1. PcMSP: A Dataset for Scientific Action Graphs Extraction from Polycrystalline Materials Synthesis Procedure Text

   Xianjun Yang, Ya Zhuo, Julia Zuo, Xinlu Zhang, Stephen Wilson, and Linda Petzold

   In Findings of the Association for Computational Linguistics: EMNLP 2022, Jul 2022

2021

1. An Analysis of Relation Extraction within Sentences from Wet Lab Protocols

   Xianjun Yang, Xinlu Zhang, Julia Zuo, Stephen Wilson, and Linda Petzold

   In 2021 IEEE International Conference on Big Data (Big Data), Jul 2021
2. On explosive boiling of a multicomponent Leidenfrost drop

   Sijia Lyu, Huanshu Tan, Yuki Wakata, Xianjun Yang, Chung K Law, Detlef Lohse, and 1 more author

   Proceedings of the National Academy of Sciences, Jul 2021

2019

1. Convective heat transfer along ratchet surfaces in vertical natural convection

   Hechuan Jiang, Xiaojue Zhu, Varghese Mathai, Xianjun Yang, Roberto Verzicco, Detlef Lohse, and 1 more author

   Journal of fluid mechanics, Jul 2019