Yoon Kim

Yoon Kim is NBX Career Development Professor and an assistant professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT. He’s also affiliated with the Computer Science and Artificial Intelligence Laboratory (CSAIL). Kim conducts research in natural language processing and machine learning. He is interested in developing efficient methods for training and deploying large-scale models, using neuro-symbolic techniques to imbue humanlike inductive biases to computational models, and investigating connections between artificial and human language processing systems. Kim received a PhD in computer science from Harvard, an MS in data science from NYU, an MA in statistics from Columbia, and dual BA degrees in mathematics and economics from Cornell.

Selected Publications

• Merrill, W., Wu, Z., Naka, N., Kim, Y., & Linzen, T. (2024). Can you learn semantics through next-word prediction? The case of entailment. In Findings of the Association for Computational Linguistics: ACL 2024. Association for Computational Linguistics.
• Karlinsky, L., Panda, R., Feris, R., Wang, Z., Kim, Y., Sun, H. (2023). Multitask Prompt Tuning Enables Parameter-Efficient Transfer Learning. International Conference on Learning Representations (ICLR).
• Cox, D., Karlinsky, L., Wang, P., Panda, R., Feris, R., Wang, Z., Kim, Y., Hennigen, L.T., and Greengard, P. (2023). Learning to Grow Pretrained Models for Efficient Transformer Training. International Conference on Learning Representations (ICLR).

Media

• September 16, 2024: MIT News, Enhancing LLM collaboration for smarter, more efficient solutions
• July 11, 2024: MIT News, Reasoning skills of large language models are often overestimated
• June 12, 2024: MIT News, Researchers use large language models to help robots navigate