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Found 6 papers, 1 papers with code
SIBO: A Simple Booster for Parameter-Efficient Fine-Tuning
Fine-tuning all parameters of large language models (LLMs) necessitates substantial computational power and extended time.
Few-Shot Learning on Graphs: from Meta-learning to Pre-training and Prompting
Finally, we outline prospective future directions for few-shot learning on graphs to catalyze continued innovation in this field.
Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks
We design a novel graph prompting function to reformulate the downstream task into a similar template as the pretext task in pre-training, thereby narrowing the objective gap.
Prompt Tuning on Graph-augmented Low-resource Text Classification
During pre-training, we propose three graph interaction-based contrastive strategies to jointly pre-train a graph-text model; during downstream classification, we explore handcrafted discrete prompts and continuous prompt tuning for the jointly pre-trained model to achieve zero- and few-shot classification, respectively.
Augmenting Low-Resource Text Classification with Graph-Grounded Pre-training and Prompting
Text classification is a fundamental problem in information retrieval with many real-world applications, such as predicting the topics of online articles and the categories of e-commerce product descriptions.
Meta-Inductive Node Classification across Graphs
That is, MI-GNN does not directly learn an inductive model; it learns the general knowledge of how to train a model for semi-supervised node classification on new graphs.
