3 citations · 6 across the 9 of their papers we have counts for
5 papers · 1 filter
On the Salience of Low-Probability Tokens for AI-Generated Text Detection: A Multiscale Uncertainty Perspective
Yikai Guo, Bin Wang, Xilai Fan +2
AI-generated text increasingly blends with human writing, raising practical risks such as misinformation, academic misuse, and corpora contamination. While statistical detectors ar…
Domain-Hierarchy Adaptation via Chain of Iterative Reasoning for Few-shot Hierarchical Text Classification
Ke Ji, Peng Wang, Wenjun Ke +4
Recently, various pre-trained language models (PLMs) have been proposed to prove their impressive performances on a wide range of few-shot tasks. However, limited by the unstructur…
Recall, Retrieve and Reason: Towards Better In-Context Relation Extraction
Guozheng Li, Peng Wang, Wenjun Ke +5
Relation extraction (RE) aims to identify relations between entities mentioned in texts. Although large language models (LLMs) have demonstrated impressive in-context learning (ICL…
CMNEE: A Large-Scale Document-Level Event Extraction Dataset based on Open-Source Chinese Military News
Mengna Zhu, Zijie Xu, Kaisheng Zeng +4
Extracting structured event knowledge, including event triggers and corresponding arguments, from military texts is fundamental to many applications, such as intelligence analysis…
Unlocking Instructive In-Context Learning with Tabular Prompting for Relational Triple Extraction
Guozheng Li, Wenjun Ke, Peng Wang +5
The in-context learning (ICL) for relational triple extraction (RTE) has achieved promising performance, but still encounters two key challenges: (1) how to design effective prompt…