31 citations · 109 across the 13 of their papers we have counts for
13 papers
Large Language Models can be Guided to Evade AI-Generated Text Detection
Ning Lu, Shengcai Liu, Rui He +3
Large language models (LLMs) have shown remarkable performance in various tasks and have been extensively utilized by the public. However, the increasing concerns regarding the mis…
Multi-Domain Learning From Insufficient Annotations
Rui He, Shengcai Liu, Jiahao Wu +2
Multi-domain learning (MDL) refers to simultaneously constructing a model or a set of models on datasets collected from different domains. Conventional approaches emphasize domain-…
Document-Level Relation Extraction with Adaptive Focal Loss and Knowledge Distillation
Qingyu Tan, Ruidan He, Lidong Bing +1
Document-level Relation Extraction (DocRE) is a more challenging task compared to its sentence-level counterpart. It aims to extract relations from multiple sentences at once. In t…
IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks
Liying Cheng, Lidong Bing, Ruidan He +3
Traditionally, a debate usually requires a manual preparation process, including reading plenty of articles, selecting the claims, identifying the stances of the claims, seeking th…
Enhancing Multilingual Language Model with Massive Multilingual Knowledge Triples
Linlin Liu, Xin Li, Ruidan He +3
Knowledge-enhanced language representation learning has shown promising results across various knowledge-intensive NLP tasks. However, prior methods are limited in efficient utiliz…
MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER
Ran Zhou, Xin Li, Ruidan He +4
Data augmentation is an effective solution to data scarcity in low-resource scenarios. However, when applied to token-level tasks such as NER, data augmentation methods often suffe…