1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.LG2025
Exploring Graph Learning Tasks with Pure LLMs: A Comprehensive Benchmark and Investigation
Yuxiang Wang, Xinnan Dai, Wenqi Fan +1
In recent years, large language models (LLMs) have emerged as promising candidates for graph tasks. Many studies leverage natural language to describe graphs and apply LLMs for rea…
cs.LG2024★ 1 cited
How Do Large Language Models Understand Graph Patterns? A Benchmark for Graph Pattern Comprehension
Xinnan Dai, Haohao Qu, Yifen Shen +6
Benchmarking the capabilities and limitations of large language models (LLMs) in graph-related tasks is becoming an increasingly popular and crucial area of research. Recent studie…
cs.CL2024
Revisiting the Graph Reasoning Ability of Large Language Models: Case Studies in Translation, Connectivity and Shortest Path
Xinnan Dai, Qihao Wen, Yifei Shen +4
Large Language Models (LLMs) have achieved great success in various reasoning tasks. In this work, we focus on the graph reasoning ability of LLMs. Although theoretical studies pro…