LLaGA: Large Language and Graph Assistant
Authors: Runjin Chen, Tong Zhao, Ajay Kumar Jaiswal, Neil Shah, Zhangyang Wang
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments across popular graph benchmarks show that LLa GA delivers outstanding performance across four datasets and three tasks using one single model, surpassing state-of-the-art graph models in both supervised and zeroshot scenarios. |
| Researcher Affiliation | Collaboration | 1The University of Texas at Austin 2Snap Inc. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. Figure 1 illustrates the LLa GA framework but is not a pseudocode representation. |
| Open Source Code | Yes | Our code is available at https: //github.com/VITA-Group/LLa GA |
| Open Datasets | Yes | Datasets. We train and evaluate our model on four widely-recognized graph datasets: ogbn-Arxiv (Hu et al., 2020), ogbn-Products (Hu et al., 2020), Pubmed, and Cora (Yang et al., 2016). |
| Dataset Splits | Yes | For node-level tasks, we adhere to the standard train/validation/test splits (Hu et al., 2020) for each dataset: 6:2:3 for Arxiv, 8:2:90 for Products, and 6:2:2 for both Pubmed and Cora. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run its experiments, such as GPU models, CPU types, or memory specifications. |
| Software Dependencies | Yes | In our model s implementation, we primarily employ Vicuna-7B-v1.5-16K (Chiang et al., 2023) as the foundational base models, and Sim Teg (Duan et al., 2023) as default text-encoding model. |
| Experiment Setup | Yes | The learning rate is consistently set to 2e-5, and the batch size is maintained at 16 for all models. We train our model for one epoch. For the Neighborhood Detail Template, we sample two-hop neighbors around each node, setting the sample size to 10 for each hop. In the Hop-Field Overview Template, 4 hop embeddings are employed to encapsulate the structural information surrounding the central node. |