Fetching the paper…
Reading the bibliography…
The latest advancements in large language models (LLMs) have revolutionized the field of natural language processing (NLP).
Giles, C.L., Bollacker, K.D., Lawrence, S.: Citeseer: An automatic citation indexing system. In: Proc. ACM Conf. Digital Libraries (1998)
1998
Earlier work this paper cites.
McCallum, A.K., Nigam, K., Rennie, J., Seymore, K.: Automating the construction of internet portals with machine learning. Information Retrieval 3
2000
Earlier work this paper cites.
Zhu, X., Ghahramani, Z., Lafferty, J.: Semi-supervised learning using gaussian fields and harmonic functions. In: Proc. Int. Conf. Machine Learning (2003)
2003
Earlier work this paper cites.
Zhou, D., Bousquet, O., Lal, T.N., Weston, J., Schölkopf, B.: Learning with local and global consistency. In: Advances in Neural Information Processing Systems (2004)
2004
Earlier work this paper cites.
Zhou, D., Schölkopf, B.: Regularization on discrete spaces. In: DAGM Symposium (2005)
2005
Earlier work this paper cites.
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., Eliassi-Rad, T.: Collective classification in network data. AI Magazine 29
2008
Earlier work this paper cites.
Hong, C., Liu, Z., Yang, J.: Sparsity induced similarity measure for label propagation. In: Proc. IEEE Int. Conf. Computer Vision (2009)
2009
Earlier work this paper cites.
Karasuyama, M., Karasuyama, H.: Manifold-based similarity adaptation for label propagation. In: Advances in Neural Information Processing Systems (2013)
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. In: Proc. Int. Conf. Learning Representations (2016)
2016
Earlier work this paper cites.
Hamilton, W.L., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems (2017)
2017
Earlier work this paper cites.
Velickovic, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., Bengio, Y.: Graph attention networks. In: Proc. Int. Conf. Learning Representations (2018)
2018
Earlier work this paper cites.
Liu, Y., Lee, J., Park, M., Kim, S., Yang, E., Hwang, S.J., Yang, Y.: Learning to propagate labels: Transductive propagation network for few-shot learning. In: Proc. Int. Conf. Learning Representations (2019)
2019
Earlier work this paper cites.
Brown, T., et al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Miaschi, A., Dell’Orletta, F.: Contextual and non-contextual word embeddings: an in-depth linguistic investigation. In: ACL (2020)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
Chen, Z., Mao, H., Li, H., Jin, W., Wen, H., Wei, X., Wang, S., Yin, D., Fan, W., Liu, H., Tang, J.: Exploring the potential of large language models (llms) in learning on graphs. In: ACM SIGKDD Explorations Newsletter. vol. 25 (2024)
2024
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
Qiu, X., Sun, T., Xu, Y., Shao, Y., Dai, N., Huang, X.: Pre-trained models for natural language processing: A survey. Science China Technological Sciences 63
2020
Cited alongside, same era.
Yang, M., Meng, Z., King, I.: L2 feature normalization for dynamic graph embedding. In: Proc. IEEE Int. Conf. Data Mining (2020)
2020
Cited alongside, same era.
Wang, H., Leskovec, J.: Combining graph convolutional neural networks and label propagation. ACM Trans. Information Systems 40
2021
Cited alongside, same era.
Chien, E., Chang, W.C., Hsieh, C.J., Yu, H.F., Zhang, J., Milenkovic, O., Dhillon, I.S.: Node feature extraction by self-supervised multi-scale neighborhood prediction. In: Proc. Int. Conf. Learning Representations (2022)
2022
Cited alongside, same era.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: NAACL (2023)
2023
Cited alongside, same era.
Lin, X., Kang, J., Cong, W., Tong, H.: Bemap: Balanced message passing for fair graph neural network. In: LoG (2023)
2023
Cited alongside, same era.
OpenAI: Gpt-4 technical report. In: arXiv:2303.08774 (2023)
2023
Cited alongside, same era.
He, X., Bresson, X., Laurent, T., Perold, A., LeCun, Y., Hooi, B.: Harnessing explanations: Llm-to-lm interpreter for enhanced text-attributed graph representation learning. In: Proc. Int. Conf. Learning Representations (2024)
2024
Closest in time.
Hu, Y., Chen, C., Yang, C.H., Li, R., Zhang, D., Chen, Z., Chng, E.: Gentranslate: Large language models are generative multilingual speech and machine translators. In: ACL (2024)
2024
Closest in time.
Mai, Z., Zhang, J., Xu, Z., Xiao, Z.: Financial sentiment analysis meets llama 3: A comprehensive analysis. In: MLMI (2024)
2024
Closest in time.
Sun, S., Ma, C.: Hyperbolic contrastive learning with model-augmentation for knowledge-aware recommendation. In: Joint European Conference on Machine Learning and Knowledge Discovery in Databases (2024)
2024
Closest in time.
Wang, Z., Wang, R., Wang, M., Lai, T., Zhang, M.: Self-supervised transformer-based pre-training method with general plant infection dataset. In: PRCV (2024)
2024
Closest in time.
Xiao, Z., Blanco, E., Huang, Y.: Analyzing large language models’ capability in location prediction. In: LREC-COLING (2024)
2024
Closest in time.
2024
Closest in time.