Lee, Chen-Yu

Found in the middle: Calibrating Positional Attention Bias Improves Long Context Utilization

Hsieh, C.-Y., Chuang, Y.-S., Li, C.-L., Wang, Z., Le, L., Kumar, A., Glass, J., Ratner, A., Lee, C.-Y., Krishna, R., et al. (2024) · Findings of the Association for Computational Linguistics ACL 2024

Topic ModelingNatural Language Processing Techniques

Cited by 31

Heterogeneous Swarms: Jointly Optimizing Model Roles and Weights for Multi-LLM Systems

Feng, S., Wang, Z., Goyal, P., Wang, Y., Shi, W., Xia, H., Palangi, H., Zettlemoyer, L., Tsvetkov, Y., Lee, C.-Y., et al. (2025) · arXiv

Topic ModelingMetaheuristic Optimization Algorithms Research

Cited by 18

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Hsieh, C.-Y., Li, C.-L., Yeh, C.-K., Nakhost, H., Fujii, Y., Ratner, A., Krishna, R., Lee, C.-Y., & Pfister, T. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 16

Found in the Middle: Calibrating Positional Attention Bias Improves Long Context Utilization

Hsieh, C.-Y., Chuang, Y.-S., Li, C.-L., Wang, Z., Le, L. T., Kumar, A., Glass, J., Ratner, A., Lee, C.-Y., Krishna, R., et al. (2024) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 0