reviews: 
Comments: In the field of artificial intelligence, creating a single model to handle diverse tasks has been a longstanding goal. Large language models have excelled in language-related tasks, but applying this versatility to graph-based tasks is challenging. Graph data from different domains have unique attributes and distributions, making uniform representation difficult. Additionally, graph tasks encompass nodes, links, and graphs, necessitating distinct strategies. Furthermore, context-aware learning for graphs lacks a clear method.

To address these challenges, this paper introduced "One for All" (OFA), a framework that uses a single graph model to conquer these obstacles. OFA uses text-attributed graphs, unifying diverse graph data and standardizing task representation with "nodes-of-interest." It pioneers a novel graph prompting approach for versatile task handling without fine-tuning. They train OFA using data from various domains and evaluate its performance in diverse learning scenarios. The results establish OFA as the first general-purpose graph classification model, revolutionizing the field of graph-based artificial intelligence.
Strengths: The motivation of this paper is well-delivered and interesting

The proposed method is novel and seems to be good according to their experimental report
Weaknesses: W1. translating graph data with text feature sequences might lose some key information from graphs. 

W2. According to Figure 1, LLM enhances text for text-attributed graphs. it still needs a GNN as a predictor. (1) I wonder whether the GNN model is also pre-trained and frozen. and (2) what are the results compared with using LLM as a predictor instead of an enhancer?

W3. It is unclear how to use NOI prompt nodes and class nodes with GNN to predict the downstream tasks. according to Figure 2, can I say that the downstream tasks (node, edge, graph classifications) are treated as predicting links between NOI prompt and class nodes? It seems that NOI prompt nodes can be treated as a special case of the prompt graph mentioned in the paper "All in One" (Sun et al., 2023), what's the differences between them? and why the authors use only one NOI prompt node instead of multiple NOI prompt nodes.

W4. I wonder why the authors use sentence transformer instead of ChatGPT API, or LLAMA, etc since they claim that "any kind of LLM can be used as the encoder, and a stronger LLM potentially yields better overall performance." Is it possible that a very large language model contains too much unrelated knowledge/intelligence that may reduce task performance?

W5. I wonder what would happen if you Re-order the item in your prompt for ChatGPT.  It seems the reason why you use sentence transformer is that it is not sensitive to the order of your prompt? if you change to ChatGPT, different orders may generate entirely different sentences, making the downstream performance unpredictable
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