Fetching the paper…
Reading the bibliography…
The recent "pre-train, prompt, predict training" paradigm has gained popularity as a way to learn generalizable models with limited labeled data.
Y. Dong, L. Deng, and G. Dahl, "Roles of pre-training and fine-tuning in context-dependent DBN-HMMs for real-world speech recognition," in Proc. NIPS Workshop on Deep Learning and Unsupervised Feature Learning
2010
Earlier work this paper cites.
K. Jacob, D. M.W. Chang, and L. K. Toutanova, "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding," in Proceedings of NAACL-HLT
2019
Earlier work this paper cites.
W. Hu, et al., "Strategies for pre-training graph neural networks." in International Conference on Learning Representations
2020
Earlier work this paper cites.
X. Feng, et al., "Graph learning: A survey," IEEE Transactions on Artificial Intelligence 2.2
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
Y. Zhang, et al., "Language Models as Recommender Systems: Evaluations and Limitations," in I (Still) Can’t Believe It’s Not Better! NeurIPS 2021 Workshop
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
S. Geng, et al., "Recommendation as language processing (rlp): A unified pretrain, personalized prompt & predict paradigm (p5)," in Proceedings of the 16th ACM Conference on Recommender Systems
2022
Earlier work this paper cites.
Y. K. Chia, L. Bing, S. Poria, and L. Si, "RelationPrompt: Leveraging Prompts to Generate Synthetic Data for Zero-Shot Relation Triplet Extraction," in Findings of the Association for Computational Linguistics: ACL
2022
Earlier work this paper cites.
B. Ryan, et al., "Improving Language Model Predictions via Prompts Enriched with Knowledge Graphs," in Workshop on Deep Learning for Knowledge Graphs (DL4KG@ ISWC2022)
2022
Earlier work this paper cites.
X. Lv, et al., "Do pre-trained models benefit knowledge graph completion? a reliable evaluation and a reasonable approach," in Findings of the Association for Computational Linguistics: ACL
2022
Cited alongside, same era.
2022
Cited alongside, same era.
H. Ye, et al., "Ontology-enhanced Prompt-tuning for Few-shot Learning," in Proceedings of the ACM Web Conference: WWW 2022
2022
Cited alongside, same era.
J. Zhou, Q. Zhang, Q. Chen, L. He, and X. Huang, "A Multi-Format Transfer Learning Model for Event Argument Extraction via Variational Information Bottleneck," Proceedings of the International Conference on Computational Linguistics
2022
Cited alongside, same era.
T. Fang, Y. Zhang, Y. Yang, and C. Wang, "Prompt Tuning for Graph Neural Networks," arXiv preprint
M. Sun, K. Zhou, X. He, Y. Wang, and X. Wang. "Gppt: Graph pre-training and prompt tuning to generalize graph neural networks," in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining: KDD
2022
Later among the works it cites.
D. Sileo, W. Vossen, and R. Raymaekers, "Zero-Shot Recommendation as Language Modeling," in Advances in Information Retrieval: 44th European Conference on IR Research, ECIR
2022
Later among the works it cites.
P. Liu, et al., "Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing," ACM Computing Surveys 55
2023
Closest in time.
2023
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022
Cited alongside, same era.
X. Chen, et al., "Knowprompt: Knowledge-aware prompt-tuning with synergistic optimization for relation extraction," in Proceedings of the ACM Web Conference: WWW 2022
2022
Cited alongside, same era.
X. Liu, et al., "Oag-bert: Pre-train heterogeneous entity-augmented academic language models," Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
2022
Cited alongside, same era.
2022
Cited alongside, same era.
X. Wang, K. Zhou, J. Wen, and W. X. Zhao, "Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning," in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining: KDD
2022
Cited alongside, same era.
H. Liu, and Y. Qin, "Heterogeneous graph prompt for Community Question Answering," Concurrency and Computation: Practice and Experience
2022
Cited alongside, same era.
L. Lei, Y. Zhang, and L. Chen, "Personalized prompt learning for explainable recommendation," ACM Transactions on Information Systems 41
2023
Closest in time.
J. Liu, et al., "KEPT: Knowledge Enhanced Prompt Tuning for event causality identification," Knowledge-Based Systems 259
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.