1 citations · 1 across the 5 of their papers we have counts for
5 papers
When Graph Tokens Sink: A Mechanistic Analysis of Graph Language Models
Ding Zhang, Runtao Zhou, Wenqing Zheng +3
Graph Language Models (GLMs) have become a promising direction for adapting Large Language Models (LLMs) to graph learning tasks. By transforming graph topology and node informatio…
Revisiting RAG Retrievers: An Information Theoretic Benchmark
Wenqing Zheng, Dmitri Kalaev, Noah Fatsi +5
Retrieval-Augmented Generation (RAG) systems rely critically on the retriever module to surface relevant context for large language models. Although numerous retrievers have recent…
LLM-Powered Text-Attributed Graph Anomaly Detection via Retrieval-Augmented Reasoning
Haoyan Xu, Ruizhi Qian, Zhengtao Yao +10
Anomaly detection on attributed graphs plays an essential role in applications such as fraud detection, intrusion monitoring, and misinformation analysis. However, text-attributed…
GRAVITY: A Framework for Personalized Text Generation via Profile-Grounded Synthetic Preferences
Priyanka Dey, Daniele Rosa, Wenqing Zheng +3
Personalization in LLMs often relies on costly human feedback or interaction logs, limiting scalability and neglecting deeper user attributes. To reduce the reliance on human annot…
Tuning-Free LLM Can Build A Strong Recommender Under Sparse Connectivity And Knowledge Gap Via Extracting Intent
Wenqing Zheng, Noah Fatsi, Daniel Barcklow +5
Recent advances in recommendation with large language models (LLMs) often rely on either commonsense augmentation at the item-category level or implicit intent modeling on existing…