Summary of Gofa: a Generative One-for-all Model For Joint Graph Language Modeling, by Lecheng Kong et al.
GOFA: A Generative One-For-All Model for Joint Graph Language Modeling
by Lecheng Kong, Jiarui Feng, Hao Liu, Chengsong Huang, Jiaxin Huang, Yixin Chen, Muhan Zhang
First submitted to arxiv on: 12 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 A novel generative graph language model, called GOFA, is proposed to address the challenges of developing a Graph Foundation Model (GFM) that can handle unlimited tasks while capturing graph structure. The model extends conventional language modeling to the graph domain by interleaving randomly initialized GNN layers into a frozen pre-trained LLM. This allows for organic combination of semantic and structural modeling abilities. GOFA is pre-trained on graph-level next-word prediction, question-answering, and structural tasks to obtain self-supervised pretraining, fluidity in tasks, and graph awareness. The fine-tuned model demonstrates strong ability to solve structural and contextual problems in zero-shot scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GOFA is a new way to understand and work with graphs. Right now, we can’t easily make models that are good at both understanding the structure of a graph and handling lots of different tasks. GOFA tries to fix this by combining two types of modeling: one that’s good at language (like talking or writing) and one that’s good at graphs. This lets it understand the meaning of words on a graph, as well as how the graph is structured. GOFA is trained using new ways to predict what comes next in a sequence of graph elements, answer questions about a graph, and solve problems based on the structure of a graph. |
Keywords
* Artificial intelligence * Gnn * Language model * Pretraining * Question answering * Self supervised * Zero shot
