Summary of Revisiting the Graph Reasoning Ability Of Large Language Models: Case Studies in Translation, Connectivity and Shortest Path, by Xinnan Dai et al.
Revisiting the Graph Reasoning Ability of Large Language Models: Case Studies in Translation, Connectivity and Shortest Path
by Xinnan Dai, Qihao Wen, Yifei Shen, Hongzhi Wen, Dongsheng Li, Jiliang Tang, Caihua Shan
First submitted to arxiv on: 18 Aug 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper investigates the ability of Large Language Models (LLMs) to reason about graph structures, specifically focusing on three fundamental graph tasks: graph description translation, graph connectivity, and the shortest-path problem. Despite theoretical studies demonstrating LLMs’ capability for graph reasoning, empirical evaluations reveal numerous failures. The authors revisit these tasks to better understand the discrepancy between theory and practice, finding that LLMs can struggle to comprehend graph structures through text descriptions and exhibit varying performance across all three tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well Large Language Models (LLMs) can understand graphs and make decisions based on them. Graphs are like maps with nodes and connections. The researchers wanted to see if these models, which are really good at understanding language, could also reason about graph structures. They tested the models on three important tasks: translating text descriptions of graphs into actual graphs, figuring out how connected different parts of a graph are, and finding the shortest path between two points. The results show that LLMs can struggle to understand graph structures when given written descriptions and perform differently on each task. |
Keywords
» Artificial intelligence » Translation
