MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs

• URL: http://arxiv.org/abs/2312.03731v6
• Date: Thu, 22 Feb 2024 06:35:04 GMT
• Title: MultiGPrompt for Multi-Task Pre-Training and Prompting on Graphs
• Authors: Xingtong Yu, Chang Zhou, Yuan Fang, Xinming Zhang
• Abstract summary: MultiGPrompt is a novel multi-task pre-training and prompting framework for graph representation learning. We propose a dual-prompt mechanism consisting of composed and open prompts to leverage task-specific and global pre-training knowledge.
• Score: 36.344668085637274
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graphs can inherently model interconnected objects on the Web, thereby facilitating a series of Web applications, such as web analyzing and content recommendation. Recently, Graph Neural Networks (GNNs) have emerged as a mainstream technique for graph representation learning. However, their efficacy within an end-to-end supervised framework is significantly tied to the availabilityof task-specific labels. To mitigate labeling costs and enhance robustness in few-shot settings, pre-training on self-supervised tasks has emerged as a promising method, while prompting has been proposed to further narrow the objective gap between pretext and downstream tasks. Although there has been some initial exploration of prompt-based learning on graphs, they primarily leverage a single pretext task, resulting in a limited subset of general knowledge that could be learned from the pre-training data. Hence, in this paper, we propose MultiGPrompt, a novel multi-task pre-training and prompting framework to exploit multiple pretext tasks for more comprehensive pre-trained knowledge. First, in pre-training, we design a set of pretext tokens to synergize multiple pretext tasks. Second, we propose a dual-prompt mechanism consisting of composed and open prompts to leverage task-specific and global pre-training knowledge, to guide downstream tasks in few-shot settings. Finally, we conduct extensive experiments on six public datasets to evaluate and analyze MultiGPrompt.

Related papers

• Generalized Graph Prompt: Toward a Unification of Pre-Training and Downstream Tasks on Graphs [20.406549548630156]
  GraphPrompt is a novel pre-training and prompting framework on graphs. It unifies pre-training and downstream tasks into a common task template. It also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-trained model.
  arXiv  Detail & Related papers  (2023-11-26T14:35:28Z)
• ULTRA-DP: Unifying Graph Pre-training with Multi-task Graph Dual Prompt [67.8934749027315]
  We propose a unified framework for graph hybrid pre-training which injects the task identification and position identification into GNNs. We also propose a novel pre-training paradigm based on a group of $k$-nearest neighbors.
  arXiv  Detail & Related papers  (2023-10-23T12:11:13Z)
• Unified Pretraining for Recommendation via Task Hypergraphs [55.98773629788986]
  We propose a novel multitask pretraining framework named Unified Pretraining for Recommendation via Task Hypergraphs. For a unified learning pattern to handle diverse requirements and nuances of various pretext tasks, we design task hypergraphs to generalize pretext tasks to hyperedge prediction. A novel transitional attention layer is devised to discriminatively learn the relevance between each pretext task and recommendation.
  arXiv  Detail & Related papers  (2023-10-20T05:33:21Z)
• TransPrompt v2: A Transferable Prompting Framework for Cross-task Text Classification [37.824031151922604]
  We propose TransPrompt v2, a novel transferable prompting framework for few-shot learning across similar or distant text classification tasks. For learning across similar tasks, we employ a multi-task meta-knowledge acquisition (MMA) procedure to train a meta-learner. For learning across distant tasks, we inject the task type descriptions into the prompt, and capture the intra-type and inter-type prompt embeddings.
  arXiv  Detail & Related papers  (2023-08-29T04:16:57Z)
• All in One: Multi-task Prompting for Graph Neural Networks [30.457491401821652]
  We propose a novel multi-task prompting method for graph models. We first unify the format of graph prompts and language prompts with the prompt token, token structure, and inserting pattern. We then study the task space of various graph applications and reformulate downstream problems to the graph-level task.
  arXiv  Detail & Related papers  (2023-07-04T06:27:31Z)
• Pre-training Multi-task Contrastive Learning Models for Scientific Literature Understanding [52.723297744257536]
  Pre-trained language models (LMs) have shown effectiveness in scientific literature understanding tasks. We propose a multi-task contrastive learning framework, SciMult, to facilitate common knowledge sharing across different literature understanding tasks.
  arXiv  Detail & Related papers  (2023-05-23T16:47:22Z)
• GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural Networks [16.455234748896157]
  GraphPrompt is a novel pre-training and prompting framework on graphs. It unifies pre-training and downstream tasks into a common task template. It also employs a learnable prompt to assist a downstream task in locating the most relevant knowledge from the pre-train model.
  arXiv  Detail & Related papers  (2023-02-16T02:51:38Z)
• Fast Inference and Transfer of Compositional Task Structures for Few-shot Task Generalization [101.72755769194677]
  We formulate it as a few-shot reinforcement learning problem where a task is characterized by a subtask graph. Our multi-task subtask graph inferencer (MTSGI) first infers the common high-level task structure in terms of the subtask graph from the training tasks. Our experiment results on 2D grid-world and complex web navigation domains show that the proposed method can learn and leverage the common underlying structure of the tasks for faster adaptation to the unseen tasks.
  arXiv  Detail & Related papers  (2022-05-25T10:44:25Z)
• Unified Multimodal Pre-training and Prompt-based Tuning for Vision-Language Understanding and Generation [86.26522210882699]
  We propose Unified multimodal pre-training for both Vision-Language understanding and generation. The proposed UniVL is capable of handling both understanding tasks and generative tasks. Our experiments show that there is a trade-off between understanding tasks and generation tasks while using the same model.
  arXiv  Detail & Related papers  (2021-12-10T14:59:06Z)
• VLM: Task-agnostic Video-Language Model Pre-training for Video Understanding [78.28397557433544]
  We present a task-agnostic multi-modal pre-training approach that can accept either video or text input, or both for a variety of end tasks. Experimental results show strong performance across a wider range of tasks than any previous methods, often outperforming task-specific pre-training.
  arXiv  Detail & Related papers  (2021-05-20T19:13:27Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.