4 citations · 6 across the 12 of their papers we have counts for
12 papers
SGG-R: From Next-Token Prediction to End-to-End Unbiased Scene Graph Generation
Jiaye Feng, Qixiang Yin, Yuankun Liu +2
Scene Graph Generation (SGG) structures visual scenes as graphs of objects and their relations. While Multimodal Large Language Models (MLLMs) have advanced end-to-end SGG, current…
CLEAR-KGQA: Clarification-Enhanced Ambiguity Resolution for Knowledge Graph Question Answering
Liqiang Wen, Guanming Xiong, Tong Mo +3
This study addresses the challenge of ambiguity in knowledge graph question answering (KGQA). While recent KGQA systems have made significant progress, particularly with the integr…
GraphSOS: Graph Sampling and Order Selection to Help LLMs Understand Graphs Better
Xu Chu, Hanlin Xue, Zhijie Tan +3
The success of Large Language Models (LLMs) in various domains has led researchers to apply them to graph-related problems by converting graph data into natural language text. Howe…
Mitigating Hallucinations on Object Attributes using Multiview Images and Negative Instructions
Zhijie Tan, Yuzhi Li, Shengwei Meng +5
Current popular Large Vision-Language Models (LVLMs) are suffering from Hallucinations on Object Attributes (HoOA), leading to incorrect determination of fine-grained attributes in…
Adaptive Spatiotemporal Augmentation for Improving Dynamic Graph Learning
Xu Chu, Hanlin Xue, Bingce Wang +5
Dynamic graph augmentation is used to improve the performance of dynamic GNNs. Most methods assume temporal locality, meaning that recent edges are more influential than earlier ed…
Domaino1s: Guiding LLM Reasoning for Explainable Answers in High-Stakes Domains
Xu Chu, Zhijie Tan, Hanlin Xue +3
Large Language Models (LLMs) are widely applied to downstream domains. However, current LLMs for high-stakes domain tasks, such as financial investment and legal QA, typically gene…
Order Matters: Exploring Order Sensitivity in Multimodal Large Language Models
Zhijie Tan, Xu Chu, Weiping Li +1
Multimodal Large Language Models (MLLMs) utilize multimodal contexts consisting of text, images, or videos to solve various multimodal tasks. However, we find that changing the ord…
Supportiveness-based Knowledge Rewriting for Retrieval-augmented Language Modeling
Zile Qiao, Wei Ye, Yong Jiang +5
Retrieval-augmented language models (RALMs) have recently shown great potential in mitigating the limitations of implicit knowledge in LLMs, such as untimely updating of the latest…
Joint Event Extraction via Structural Semantic Matching
Haochen Li, Tianhao Gao, Jingkun Wang +1
Event Extraction (EE) is one of the essential tasks in information extraction, which aims to detect event mentions from text and find the corresponding argument roles. The EE task…
Building a Modal-balanced BlockChain with Semantic Reconstruction
Zhijie Tan, Xiang Yuan, Shengwei Meng +4
The current large blockchain systems (BTC Lightning network, Ethereum, etc.) are generally facing the problems of low persistence rates and high storage costs. Therefore, users ten…
Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering Over Knowledge Graphs
Zile Qiao, Wei Ye, Tong Zhang +3
Answering natural language questions on knowledge graphs (KGQA) remains a great challenge in terms of understanding complex questions via multi-hop reasoning. Previous efforts usua…
Eliciting Knowledge from Pretrained Language Models for Prototypical Prompt Verbalizer
Yinyi Wei, Tong Mo, Yongtao Jiang +2
Recent advances on prompt-tuning cast few-shot classification tasks as a masked language modeling problem. By wrapping input into a template and using a verbalizer which constructs…