reviews: 
Comments: This paper proposes One-for-All (OFA), a framework for building and training a single graph model that can perform various graph-related tasks across different domains. The experimental results on real-world datasets demonstrate the effectiveness and efficiency of the proposed framework.
Strengths: 1.	A unified graph model is designed for various tasks and different domains of graph data.

2.	The introduced model can encode features of different graphs into the same embedding space.

3.	Extensive experiments are conducted on various settings and datasets from different domains.
Weaknesses: 1.	The design of prompt nodes and utilizing NOI-subgraph to unify different tasks seem to be similar to several previous works. For example,  [1] introduced a learnable prompt vector to unify tasks, and [2-3] proposed to utilize super nodes to perform pooling readout. The proposed NOI prompt node appears to be a combination of these two lines of works. Could you include more in-depth discussions and comparisons with these related works.

2.	Comparison with methodologies that integrate LLMs and graph models, such as [4-7], is necessary. 

3.	The Few-shot and Zero-shot results, especially for TLP-SURGL and TLP-BGRL, do not outperform baseline models. Could you include further discussions and analysis of these underwhelming performance results? 

4. The ability of the LLMs to unify the feature space across datasets from diverse domains is very interesting. It would be insightful to see the unified feature representations of datasets within the same domain (e.g., two citation networks) and the datasets spanning different domains (e.g., a citation network and a molecular network). For example, some visual analysis will offer insights into the effectiveness of the LLM in harmonizing feature spaces across heterogeneous datasets.

5. Additional ablation studies can be conducted to see whether the constructed graph structure is helpful. For example, directly using the unified node features (produced by LLMs) as the input feature of some backbone GNN models like GIN, GCN, GAT.

6. The article introduces a method to employ LLMs to generate inputs for GNNs. Another line of works aims to utilize GNNs to generate inputs for LLMs, and it seems that this approach can better utilized LLMs' significant capabilities in addressing various problems on vast data. I'm curious about the difference between these two pipelines. Can you provide some explanations and compare the performance of these two pipelines.



[1] Zemin Liu, Xingtong Yu, Yuan Fang, and Xinming Zhang. Graphprompt: Unifying pre-training and downstream tasks for graph neural networks. In WWW, 2023.

[2] F. Hu, Y. Zhu, S. Wu, and T. Tan. Hierarchical graph convolutional networks for semi-supervised node classification. In IJCAI, 2019.

[3] Matthias Fey, Jan-Gin Yuen and Frank Weichert. Hierarchical Inter-Message Passing for Learning on Molecular Graphs. In ICML, 2020.

[4] Xiaoxin He, Xavier Bresson, Thomas Laurent, and Bryan Hooi. Explanations as features: Llm-based features for text-attributed graphs. arXiv preprint arXiv:2305.19523, 2023.

[5] Jianan Zhao, Meng Qu, Chaozhuo Li, Hao Yan, Qian Liu, Rui Li, Xing Xie, and Jian Tang. Learning on large-scale text-attributed graphs via variational inference. arXiv preprint arXiv:2210.14709, 2022.

[6] Ruosong Ye, Caiqi Zhang, Runhui Wang, Shuyuan Xu, and Yongfeng Zhang. Natural language is all a graph needs. arXiv preprint arXiv:2308.07134, 2023.

[7] Jianan Zhao, Le Zhuo, Yikang Shen, Meng Qu, Kai Liu, Michael Bronstein, Zhaocheng Zhu, and Jian Tang. Graphtext: Graph reasoning in text space. arXiv preprint arXiv:2310.01089, 2023.
Recommendation: 6: marginally above the acceptance threshold
Reviewer confidence: 4: You are confident in your assessment, but not absolutely certain. It is unlikely, but not impossible, that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work.
Soundness/correctness: 3 good
Clarity: 3 good