4 citations · 7 across the 8 of their papers we have counts for
5 papers · 1 filter
OneNet: A Fine-Tuning Free Framework for Few-Shot Entity Linking via Large Language Model Prompting
Xukai Liu, Ye Liu, Kai Zhang +3
Entity Linking (EL) is the process of associating ambiguous textual mentions to specific entities in a knowledge base. Traditional EL methods heavily rely on large datasets to enha…
Leveraging Entity Information for Cross-Modality Correlation Learning: The Entity-Guided Multimodal Summarization
Yanghai Zhang, Ye Liu, Shiwei Wu +4
The rapid increase in multimedia data has spurred advancements in Multimodal Summarization with Multimodal Output (MSMO), which aims to produce a multimodal summary that integrates…
Empowering Few-Shot Relation Extraction with The Integration of Traditional RE Methods and Large Language Models
Ye Liu, Kai Zhang, Aoran Gan +4
Few-Shot Relation Extraction (FSRE), a subtask of Relation Extraction (RE) that utilizes limited training instances, appeals to more researchers in Natural Language Processing (NLP…
Learn while Unlearn: An Iterative Unlearning Framework for Generative Language Models
Haoyu Tang, Ye Liu, Xi Zhao +5
Recent advances in machine learning, particularly in Natural Language Processing (NLP), have produced powerful models trained on vast datasets. However, these models risk leaking s…
Detect, Investigate, Judge and Determine: A Knowledge-guided Framework for Few-shot Fake News Detection
Ye Liu, Jiajun Zhu, Xukai Liu +5
Few-Shot Fake News Detection (FS-FND) aims to distinguish inaccurate news from real ones in extremely low-resource scenarios. This task has garnered increased attention due to the…