Cho, Kyunghyun

Neural Machine Translation with Byte-Level Subwords

Wang, C., Cho, K., & Gu, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Natural Language Processing TechniquesTopic Modeling

Cited by 147

Unsupervised Question Decomposition for Question Answering

Perez, E., Lewis, P., Yih, W.-T., Cho, K., & Kiela, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 123

QCD-aware recursive neural networks for jet physics

Louppe, G., Cho, K., Becot, C., & Cranmer, K. (2019) · Journal of High Energy Physics

Particle physics theoretical and experimental studiesHigh-Energy Particle Collisions Research

Cited by 123

Task-Oriented Query Reformulation with Reinforcement Learning

Nogueira, R., & Cho, K. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Information Retrieval and Search BehaviorRecommender Systems and Techniques

Cited by 120

Query-Efficient Imitation Learning for End-to-End Autonomous Driving

Zhang, J., & Cho, K. (2016) · arXiv

Reinforcement Learning in RoboticsAutonomous Vehicle Technology and Safety

Cited by 118

Importance of Search and Evaluation Strategies in Neural Dialogue Modeling

Kulikov, I., Miller, A., Cho, K., & Weston, J. (2019) · Proceedings of the 12th International Conference on Natural Language Generation

Speech and dialogue systemsSpeech Recognition and Synthesis

Cited by 83

Length-Adaptive Transformer: Train Once with Length Drop, Use Anytime with Search

Kim, G., & Cho, K. (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)

Topic ModelingNatural Language Processing Techniques

Cited by 59

The Effects of Noisy Labels on Deep Convolutional Neural Networks for Music Tagging

Choi, K., Fazekas, G., Cho, K., & Sandler, M. (2018) · IEEE Transactions on Emerging Topics in Computational Intelligence

Music and Audio ProcessingMachine Learning and Data Classification

Cited by 42

Differences between human and machine perception in medical diagnosis

Makino, T., Jastrzębski, S., Oleszkiewicz, W., Chacko, C., Ehrenpreis, R., Samreen, N., Chhor, C., Kim, E., Lee, J., Pysarenko, K., et al. (2022) · Scientific Reports

AI in cancer detectionExplainable Artificial Intelligence (XAI)

Cited by 30

Finding Generalizable Evidence by Learning to Convince Q&A Models

Perez, E., Karamcheti, S., Fergus, R., Weston, J., Kiela, D., & Cho, K. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 27

Comparing Test Sets with Item Response Theory

Vania, C., Htut, P. M., Huang, W., Mungra, D., Pang, R. Y., Phang, J., Liu, H., Cho, K., & Bowman, S. R. (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)

Natural Language Processing TechniquesTopic Modeling

Cited by 18

Self-Rewarding Language Models

Yuan, W., Pang, R. Y., Cho, K., Li, X., Sukhbaatar, S., Xu, J., & Weston, J. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 18

Peer reviews of peer reviews: A randomized controlled trial and other experiments

Goldberg, A., Stelmakh, I., Cho, K., Oh, A., Agarwal, A., Belgrave, D., & Shah, N. B. (2025) · PLOS ONE

Meta-analysis and systematic reviewsPublishing and Scholarly Communication

Cited by 18

Iterative Reasoning Preference Optimization

Pang, R. Y., Yuan, W., Cho, K., He, H., Sukhbaatar, S., & Weston, J. (2024) · arXiv

Constraint Satisfaction and OptimizationLogic, Reasoning, and Knowledge

Cited by 15

Training Language Models with Language Feedback at Scale

Scheurer, J., Campos, J. A., Korbak, T., Chan, J. S., Chen, A., Cho, K., & Perez, E. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 14

Training Language Models with Language Feedback

Scheurer, J., Campos, J. A., Chan, J. S., Chen, A., Cho, K., & Perez, E. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 11

Two Failures of Self-Consistency in the Multi-Step Reasoning of LLMs

Chen, A., Phang, J., Parrish, A., Padmakumar, V., Zhao, C., Bowman, S. R., & Cho, K. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 7

Towards Music Captioning: Generating Music Playlist Descriptions

Choi, K., Fazekas, G., McFee, B., Cho, K., & Sandler, M. (2016) · arXiv

Music and Audio ProcessingRecommender Systems and Techniques

Cited by 5

First Tragedy, then Parse: History Repeats Itself in the New Era of Large Language Models

Saphra, N., Fleisig, E., Cho, K., & Lopez, A. (2023) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 1

Leveraging Implicit Feedback from Deployment Data in Dialogue

Pang, R. Y., Roller, S., Cho, K., He, H., & Weston, J. (2023) · arXiv

Speech and dialogue systemsAI in Service Interactions

Cited by 1

Learning from Reward-Free Offline Data: A Case for Planning with Latent Dynamics Models

Sobal, V., Zhang, W., Cho, K., Balestriero, R., Rudner, T. G. J., & LeCun, Y. (2025) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 1