Summary of Can Large Language Models Analyze Graphs Like Professionals? a Benchmark, Datasets and Models, by Xin Sky Li et al.
Can Large Language Models Analyze Graphs like Professionals? A Benchmark, Datasets and Models
by Xin Sky Li, Weize Chen, Qizhi Chu, Haopeng Li, Zhaojun Sun, Ran Li, Chen Qian, Yiwei Wei, Zhiyuan Liu, Chuan Shi, Maosong Sun, Cheng Yang
First submitted to arxiv on: 29 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces ProGraph, a benchmark for evaluating large language models (LLMs) on graph analysis tasks. Unlike current benchmarks that require LLMs to reason over raw inputs, ProGraph expects solutions based on programming using popular libraries. The results show that the performance of current LLMs is unsatisfactory, with the best model achieving only 36% accuracy. To bridge this gap, the authors propose LLM4Graph datasets, which include crawled documents and auto-generated codes based on six widely used graph libraries. By fine-tuning open-source LLMs on these codes, they show absolute improvements in accuracies of up to 32%. The paper highlights the need for more research into LLMs’ capabilities with structured data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to test how good language models are at analyzing graphs. Graphs are like maps that show relationships between things, and they’re important in many fields. Right now, there’s no good way to test language models on this kind of task. The authors created a special dataset with different kinds of graph problems and asked if language models could solve them. They found out that the best model only got 36% of the answers right! To make it better, they came up with a new approach to train the models using real-world data. This helped the models get much better at solving graph problems. |
Keywords
» Artificial intelligence » Fine tuning
