2 citations · 3 across the 9 of their papers we have counts for
5 papers · 1 filter
GCoder: Improving Large Language Model for Generalized Graph Problem Solving
Qifan Zhang, Xiaobin Hong, Jianheng Tang +5
Large Language Models (LLMs) have demonstrated strong reasoning abilities, making them suitable for complex tasks such as graph computation. Traditional reasoning steps paradigm fo…
GLBench: A Comprehensive Benchmark for Graph with Large Language Models
Yuhan Li, Peisong Wang, Xiao Zhu +5
The emergence of large language models (LLMs) has revolutionized the way we interact with graphs, leading to a new paradigm called GraphLLM. Despite the rapid development of GraphL…
GraphArena: Evaluating and Exploring Large Language Models on Graph Computation
Jianheng Tang, Qifan Zhang, Yuhan Li +2
The ``arms race'' of Large Language Models (LLMs) demands new benchmarks to examine their progresses. In this paper, we introduce GraphArena, a benchmarking tool designed to evalua…
GraphWiz: An Instruction-Following Language Model for Graph Problems
Nuo Chen, Yuhan Li, Jianheng Tang +1
Large language models (LLMs) have achieved impressive success across several fields, but their proficiency in understanding and resolving complex graph problems is less explored. T…
ZeroG: Investigating Cross-dataset Zero-shot Transferability in Graphs
Yuhan Li, Peisong Wang, Zhixun Li +2
With the development of foundation models such as large language models, zero-shot transfer learning has become increasingly significant. This is highlighted by the generative capa…