Kati8/ICLR_2023_2025 huggingface dataset:
authors: Hao Liu
authors: Jiarui Feng
authors: Lecheng Kong
authors: Ningyue Liang
authors: Yixin Chen
authors: Muhan Zhang
reviews: 
Comments: The paper proposes One for All (OFA), a novel framework that addresses how to unify various graph tasks with various graph data.  OFA uses text-attributed graphs, allowing nodes and edges to be described in natural language, and introduces a new graph prompting method for diverse tasks without fine-tuning. When trained across multiple graph data domains, OFA demonstrates strong performance, making it a pioneering multi-purpose graph classification model.
Strengths: 1.LLM and graph prompting are both highly effective methods in their respective domains. Combining the two to address the cross-domain TAG problem is both convincing and well-motivated. I highly appreciate the idea of this paper, which is quite enlightening.
2. This paper is well-articulated.
Weaknesses: 1. A work on graph prompt learning should be cited and discussed.
* Tan, Zhen, et al. "Virtual Node Tuning for Few-shot Node Classification." arXiv preprint arXiv:2306.06063 (2023).
2. I highly recommend the authors to make the code public for readers to follow the work.
3. I suggest exploring non-meta-learning scenario such as in GraphPrompt.
* Liu, Zemin, et al. "Graphprompt: Unifying pre-training and downstream tasks for graph neural networks." Proceedings of the ACM Web Conference 2023. 2023.
Recommendation: 10: strong accept, should be highlighted at the conference
Reviewer confidence: 5: You are absolutely certain about your assessment. You are very familiar with the related work and checked the math/other details carefully.
Soundness/correctness: 3 good
Clarity: 4 excellent
reviews: 
Comments: This paper proposes a unified paradigm for learning different tasks over graphs and across different domains. Notably, the proposed algorithms achieve competitive performance in various tasks.
Strengths: 1. The framework proposed by the author is very interesting. The idea of prompted nodes naturally unifies the three major tasks in one simple but powerful problem formulation. 
2. The presentation of the result is clear with both quantitative and qualitative results.
Weaknesses: 1. The soundness of the experiments seems lacking. Based on my understanding, LLM is one if the key component to achieve cross-task and cross-domain generalization. However, the choice of LLM is rarely discussed. I didn't find detailed description of the LLM used in the main experiments. Neither a comparison between different LLMs is missing in main paper and appendix. 
2. The modeling of link-level task lack details. For example, do you have two or one class node?
Recommendation: 6: marginally above the acceptance threshold
Reviewer confidence: 3: You are fairly confident in your assessment. It is possible that you did not understand some parts of the submission or that you are unfamiliar with some pieces of related work. Math/other details were not carefully checked.
Soundness/correctness: 2 fair
Clarity: 3 good