Kim, Yoon

Convolutional Neural Networks for Sentence Classification

Kim, Y. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingSentiment Analysis and Opinion Mining

Cited by 14,831

Character-Aware Neural Language Models

Kim, Y., Jernite, Y., Sontag, D., & Rush, A. M. (2015) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 1,530

Sequence-Level Knowledge Distillation

Kim, Y., & Rush, A. M. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesAdvanced Neural Network Applications

Cited by 836

Structured Attention Networks

Kim, Y., Denton, C., Hoang, L., & Rush, A. M. (2017) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 381

Large language models are few-shot clinical information extractors

Agrawal, M., Hegselmann, S., Lang, H., Kim, Y., & Sontag, D. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Machine Learning in HealthcareTopic Modeling

Cited by 358

Temporal Analysis of Language through Neural Language Models

Kim, Y., Chiu, Y.-I., Hanaki, K., Hegde, D., & Petrov, S. (2014) · Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science

Topic ModelingDigital Humanities and Scholarship

Cited by 305

Parameter-Efficient Transfer Learning with Diff Pruning

Guo, D., Rush, A., & Kim, Y. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 220

Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks

Wu, Z., Qiu, L., Ross, A., Akyürek, E., Chen, B., Wang, B., Kim, N., Andreas, J., & Kim, Y. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 58

DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models

Chuang, Y.-S., Xie, Y., Luo, H., Kim, Y., Glass, J., & He, P. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 29

Parameter-Efficient Transfer Learning with Diff Pruning

Guo, D., Rush, A. M., & Kim, Y. (2020) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 20

Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks

Wu, Z., Qiu, L., Ross, A., Akyürek, E., Chen, B., Wang, B., Kim, N., Andreas, J., & Kim, Y. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 19

Exploring the MIT Mathematics and EECS Curriculum Using Large Language Models

Zhang, S. J., Florin, S., Lee, A. N., Niknafs, E., Marginean, A., Wang, A., Tyser, K., Chin, Z., Hicke, Y., Singh, N., et al. (2023) · arXiv

Artificial Intelligence in EducationTopic Modeling

Cited by 19

Parallelizing Linear Transformers with the Delta Rule over Sequence Length

Yang, S., Wang, B., Zhang, Y., Shen, Y., & Kim, Y. (2024) · arXiv

Parallel Computing and Optimization TechniquesAdvanced Neural Network Applications

Cited by 12

Gated Linear Attention Transformers with Hardware-Efficient Training

Yang, S., Wang, B., Shen, Y., Panda, R., & Kim, Y. (2023) · arXiv

Topic ModelingAdvanced Neural Network Applications

Cited by 10

What Do Language Models Hear? Probing for Auditory Representations in Language Models

Ngo, J., & Kim, Y. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 5

The Surprising Effectiveness of Test-Time Training for Few-Shot Learning

Akyürek, E., Damani, M., Zweiger, A., Qiu, L., Guo, H., Pari, J., Kim, Y., & Andreas, J. (2024) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 4

Data Engineering for Scaling Language Models to 128K Context

Fu, Y., Panda, R., Niu, X., Yue, X., Hajishirzi, H., Kim, Y., & Peng, H. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 3

In-Context Language Learning: Architectures and Algorithms

Akyürek, E., Wang, B., Kim, Y., & Andreas, J. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 3