Summary of Can We Soft Prompt Llms For Graph Learning Tasks?, by Zheyuan Liu et al.
Can we Soft Prompt LLMs for Graph Learning Tasks?
by Zheyuan Liu, Xiaoxin He, Yijun Tian, Nitesh V. Chawla
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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 This paper explores the potential of Large Language Models (LLMs) in understanding graph information. By introducing GraphPrompter, a novel framework that aligns graph and textual modalities, the authors demonstrate the effectiveness of LLMs in node classification and link prediction tasks on various benchmark datasets. The framework consists of two main components: a graph neural network for encoding complex graph information and an LLM for processing textual data. By leveraging the strengths of both models, GraphPrompter showcases the capabilities of LLMs as predictors in graph-related scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GraphPrompter is a new way to help computers understand graphs, which are important for things like social networks and biology. The idea is to use Large Language Models (LLMs), which are good at understanding text, but also need to work with graphs. To do this, GraphPrompter uses two parts: one that looks at the graph structure and another that works with words. By combining these parts, researchers can use LLMs to help solve problems related to graphs. |
Keywords
* Artificial intelligence * Classification * Graph neural network
