18 citations · 26 across the 8 of their papers we have counts for
4 papers · 1 filter
GABench: A Comprehensive Benchmark for Evaluating LLM Agents on Graph Analysis Tasks
Jiarui Tan, Zhongjian Zhang, YaBo Guo +5
Large language model (LLM) agents are increasingly capable of planning, using tools, and interacting with external environments. They are typically supported by harnesses, which ma…
CoEvoT: Co-Evolving Chain-of-Thought Prompting for Graph-LLM Reasoning
Haohua Niu, Xingtong Yu, Yang Liu +6
Graph learning under distribution shift presents a persistent challenge, where models adapt to new graphs with limited or even no supervision. Recent graph--LLM approaches move tow…
Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding
Zhongjian Zhang, Yue Yu, Mengmei Zhang +3
The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph tasks. As a widely recognized paradigm, Gra…
Toward Graph-Tokenizing Large Language Models with Reconstructive Graph Instruction Tuning
Zhongjian Zhang, Xiao Wang, Mengmei Zhang +2
The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph-related tasks, with the ultimate goal of de…