Summary of Graph Chain-of-thought: Augmenting Large Language Models by Reasoning on Graphs, By Bowen Jin et al.
Graph Chain-of-Thought: Augmenting Large Language Models by Reasoning on Graphs
by Bowen Jin, Chulin Xie, Jiawei Zhang, Kashob Kumar Roy, Yu Zhang, Zheng Li, Ruirui Li, Xianfeng Tang, Suhang Wang, Yu Meng, Jiawei Han
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Information Retrieval (cs.IR); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper proposes a new framework called Graph Chain-of-thought (Graph-CoT) to augment large language models (LLMs) with graph-based knowledge. The existing LLMs suffer from hallucinations, particularly on knowledge-intensive tasks, which can be alleviated by incorporating external knowledge graphs. The authors construct a Graph Reasoning Benchmark dataset called GRBench, comprising 1,740 questions that require reasoning on 10 domain graphs. They then develop a simple and effective framework to augment LLMs with these graphs, consisting of three sub-steps: LLM reasoning, LLM-graph interaction, and graph execution. The authors conduct experiments using three LLM backbones on GRBench, demonstrating the consistent outperformance of Graph-CoT over baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how to make large language models better at answering questions by giving them more information from special kinds of graphs. Right now, these models can be very good at some things but not so great at others because they don’t always know what’s going on behind the scenes. The researchers created a special test set with 1,740 questions that need to use knowledge from different types of graph connections. Then, they came up with a simple way to make language models work better by having them reason about these graphs in an iterative process. In experiments, this new approach did really well compared to earlier methods. |
