9 papers
Co-adaptive Rule Routing and Constraining for Structured Knowledge Retrieval
Zhisheng Qi, Haoyu Han, Jiaxi Li +9
Structured knowledge retrieval acquires task-relevant evidence from structured knowledge sources for downstream tasks. Existing methods rely on unconstrained retrieval or predefine…
MolP2SBench: Do LLMs Really Reason from Molecular Properties to Structure?
Yuhang Xie, Zhiyi Shi, Zeyi Zhang +4
Large language models (LLMs) are increasingly used in molecular discovery and structure elucidation, where decisions depend on physical observations. Existing evaluations primarily…
Towards Explainable Benchmarking for Data-driven Post-Wildfire Debris Flow Prediction
Zhisheng Qi, Li Zhu, Utkarsh Sahu +3
Post-wildfire debris flows (PFDFs) are destructive sediment-laden hazards triggered when intense rainfall strikes recently burned terrain, destabilizing hillslopes and threatening…
Benchmarking Multi-Modal Graph-based Social Media Popularity Prediction
Utkarsh Sahu, Zhisheng Qi, Li Zhu +4
Social media popularity prediction aims to forecast the future reach or influence of online content from early-stage observations. Accurate prediction enables key downstream applic…
RL-Index: Reinforcement Learning for Retrieval Index Reasoning
Yongjia Lei, Nedim Lipka, Zhisheng Qi +7
Retrieving external knowledge is crucial for real-world tasks but remains difficult when queries and relevant knowledge are linked by implicit reasoning (e.g., shared theorems or c…
Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation
Zhisheng Qi, Utkarsh Sahu, Li Ma +9
Retrieval-Augmented Generation (RAG) has become a cornerstone of knowledge-intensive applications, including enterprise chatbots, healthcare assistants, and agentic memory manageme…
Knowledge Homophily in Large Language Models
Utkarsh Sahu, Zhisheng Qi, Mahantesh Halappanavar +6
Large Language Models (LLMs) have been increasingly studied as neural knowledge bases for supporting knowledge-intensive applications such as question answering and fact checking.…
A Graph Perspective to Probe Structural Patterns of Knowledge in Large Language Models
Utkarsh Sahu, Zhisheng Qi, Yongjia Lei +6
Large language models have been extensively studied as neural knowledge bases for their knowledge access, editability, reasoning, and explainability. However, few works focus on th…
RAG vs. GraphRAG: A Systematic Evaluation and Key Insights
Haoyu Han, Li Ma, Yu Wang +9
Retrieval-Augmented Generation (RAG) improves large language models (LLMs) by retrieving relevant information from external sources and has been widely adopted for text-based tasks…