8 papers
In-Context Probing for Membership Inference in Fine-Tuned Language Models
Zhexi Lu, Hongliang Chi, Nathalie Baracaldo +3
Membership inference attacks (MIAs) pose a critical privacy threat to fine-tuned large language models (LLMs), especially when models are adapted to domain-specific tasks using sen…
Diagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarking
Liangliang Zhang, Zhuorui Jiang, Hongliang Chi +8
Knowledge Graph Question Answering (KGQA) systems rely on high-quality benchmarks to evaluate complex multi-hop reasoning. However, despite their widespread use, popular datasets s…
Shapley-Guided Utility Learning for Effective Graph Inference Data Valuation
Hongliang Chi, Qiong Wu, Zhengyi Zhou +1
Graph Neural Networks (GNNs) have demonstrated remarkable performance in various graph-based machine learning tasks, yet evaluating the importance of neighbors of testing nodes rem…
Unifying and Optimizing Data Values for Selection via Sequential Decision-Making
Hongliang Chi, Qiong Wu, Zhengyi Zhou +3
Data selection has emerged as a crucial downstream application of data valuation, yet the theoretical foundations for using data values in selection remain underexplored. We reform…
Precedence-Constrained Winter Value for Effective Graph Data Valuation
Hongliang Chi, Wei Jin, Charu Aggarwal +1
Data valuation is essential for quantifying data's worth, aiding in assessing data quality and determining fair compensation. While existing data valuation methods have proven effe…
Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks
Qian Ma, Hongliang Chi, Hengrui Zhang +6
The rise of self-supervised learning, which operates without the need for labeled data, has garnered significant interest within the graph learning community. This enthusiasm has l…
Active Learning for Graphs with Noisy Structures
Hongliang Chi, Cong Qi, Suhang Wang +1
Graph Neural Networks (GNNs) have seen significant success in tasks such as node classification, largely contingent upon the availability of sufficient labeled nodes. Yet, the exce…
Enhancing Graph Contrastive Learning with Node Similarity
Hongliang Chi, Yao Ma
Graph Neural Networks (GNNs) have achieved great success in learning graph representations and thus facilitating various graph-related tasks. However, most GNN methods adopt a supe…