Summary of Graph Reasoning with Large Language Models Via Pseudo-code Prompting, by Konstantinos Skianis et al.
Graph Reasoning with Large Language Models via Pseudo-code Prompting
by Konstantinos Skianis, Giannis Nikolentzos, Michalis Vazirgiannis
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed study investigates the effectiveness of prompting large language models (LLMs) with pseudo-code instructions to improve their performance in solving graph-related tasks. Building upon the recent success of LLMs in natural language processing, this work explores whether LLMs can be trained to solve seemingly simple graph problems like counting connected components or computing shortest path distances between nodes. By leveraging pseudo-code prompts, the authors demonstrate that all considered LLMs exhibit improved performance on these graph tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows how large language models (LLMs) can be taught to solve simple graph problems by using special instructions called pseudo-code. The researchers found that when they gave LLMs these instructions, it helped them do better at tasks like counting the number of connected groups in a graph or finding the shortest path between two points. This is important because it shows that LLMs can be useful for solving more complex problems. |
Keywords
» Artificial intelligence » Natural language processing » Prompting
