27 citations · 28 across the 7 of their papers we have counts for
7 papers
Graph-Based Personalized Memory for LLM Agents: Representation, Evolution, Retrieval, and Evaluation
Dac Duy Anh Nguyen, Zhangchi Qiu, Shigeng Chen +1
Large Language Model (LLM) agents are evolving from single-session tools toward long-term personal assistants that must adapt to individual users across tasks, contexts, and intera…
Text2GraphQuery-Bench: A Text to Graph Query Benchmark
Songlin Lyu, Lujie Ban, Zihang Wu +12
Graph models are fundamental to data analysis in domains rich with complex relationships. Unlike SQL, which benefits from a rel- atively unified standard and widespread familiarity…
G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge
Linhao Luo, Zicheng Zhao, Junnan Liu +9
Large language models (LLMs) excel at complex reasoning but remain limited by static and incomplete parametric knowledge. Retrieval-augmented generation (RAG) mitigates this by inc…
Beyond Memorization: A Rigorous Evaluation Framework for Medical Knowledge Editing
Shigeng Chen, Linhao Luo, Zhangchi Qiu +3
Recently, knowledge editing (KE) has emerged as a promising approach to update specific facts in Large Language Models (LLMs) without the need for full retraining. Despite the effe…
Graph Retrieval-Augmented LLM for Conversational Recommendation Systems
Zhangchi Qiu, Linhao Luo, Zicheng Zhao +2
Conversational Recommender Systems (CRSs) have emerged as a transformative paradigm for offering personalized recommendations through natural language dialogue. However, they face…
Reasoning over User Preferences: Knowledge Graph-Augmented LLMs for Explainable Conversational Recommendations
Zhangchi Qiu, Linhao Luo, Shirui Pan +1
Conversational Recommender Systems (CRSs) aim to provide personalized recommendations by capturing user preferences through interactive dialogues. Explainability in CRSs is crucial…
Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems
Zhangchi Qiu, Ye Tao, Shirui Pan +1
Conversational recommender systems (CRS) utilize natural language interactions and dialogue history to infer user preferences and provide accurate recommendations. Due to the limit…