3 citations · 3 across the 7 of their papers we have counts for
8 papers
BioMiner: A Multi-modal System for Automated Mining of Protein-Ligand Bioactivity Data from Literature
Jiaxian Yan, Jintao Zhu, Yuhang Yang +8
Protein-ligand bioactivity data published in the literature are essential for drug discovery, yet manual curation struggles to keep pace with rapidly growing literature. Automated…
Layer-Order Inversion: Rethinking Latent Multi-Hop Reasoning in Large Language Models
Xukai Liu, Ye Liu, Jipeng Zhang +3
Large language models (LLMs) perform well on multi-hop reasoning, yet how they internally compose multiple facts remains unclear. Recent work proposes \emph{hop-aligned circuit hyp…
PPTBench: Towards Holistic Evaluation of Large Language Models for PowerPoint Layout and Design Understanding
Zheng Huang, Xukai Liu, Tianyu Hu +2
PowerPoint presentations combine rich textual content with structured visual layouts, making them a natural testbed for evaluating the multimodal reasoning and layout understanding…
Know3-RAG: A Knowledge-aware RAG Framework with Adaptive Retrieval, Generation, and Filtering
Xukai Liu, Ye Liu, Shiwen Wu +4
Recent advances in large language models (LLMs) have led to impressive progress in natural language generation, yet their tendency to produce hallucinated or unsubstantiated conten…
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…
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…