  Large language models (LLMs), while exhibiting exceptional performance,
suffer from hallucinations, especially on knowledge-intensive tasks. Existing
works propose to augment LLMs with individual text units retrieved from
external knowledge corpora to alleviate the issue. However, in many domains,
texts are interconnected (e.g., academic papers in a bibliographic graph are
linked by citations and co-authorships) which form a (text-attributed) graph.
The knowledge in such graphs is encoded not only in single texts/nodes but also
in their associated connections. To facilitate the research of augmenting LLMs
with graphs, we manually construct a Graph Reasoning Benchmark dataset called
GRBench, containing 1,740 questions that can be answered with the knowledge
from 10 domain graphs. Then, we propose a simple and effective framework called
Graph Chain-of-thought (Graph-CoT) to augment LLMs with graphs by encouraging
LLMs to reason on the graph iteratively. Each Graph-CoT iteration consists of
three sub-steps: LLM reasoning, LLM-graph interaction, and graph execution. We
conduct systematic experiments with three LLM backbones on GRBench, where
Graph-CoT outperforms the baselines consistently. The code is available at
https://github.com/PeterGriffinJin/Graph-CoT.
