Summary of Are Large-language Models Graph Algorithmic Reasoners?, by Alexander K Taylor et al.
Are Large-Language Models Graph Algorithmic Reasoners?
by Alexander K Taylor, Anthony Cuturrufo, Vishal Yathish, Mingyu Derek Ma, Wei Wang
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The paper aims to address the limitations of Large Language Models (LLMs) in solving reasoning problems on explicit graphs by introducing a novel benchmark, MAGMA. This benchmark evaluates LLM performance on classical algorithmic reasoning tasks, including Breadth-First Search (BFS), Depth-First Search (DFS), Dijkstra’s algorithm, Floyd-Warshall algorithm, and Prim’s Minimum Spanning Tree (MST-Prim’s) algorithm. The authors assess the capabilities of state-of-the-art LLMs in executing these algorithms step-by-step, highlighting their persistent challenges in this domain. To overcome these limitations, the paper emphasizes the need for advanced prompting techniques and algorithmic instruction to enhance LLM graph reasoning abilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about helping computers solve problems on graphs better. Large Language Models are good at some things, but they struggle with certain types of graph problems that require multiple steps. To help them improve, researchers created a special test called MAGMA that checks how well these models can follow instructions to solve graph problems. They tested different models and found that they still have trouble with this kind of problem-solving. The researchers think that by giving the models better guidance and teaching them specific algorithms, we can help them get better at solving graph problems. |
Keywords
» Artificial intelligence » Prompting
