Le, Long

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

Speculative RAG: Enhancing Retrieval Augmented Generation through Drafting

Wang, Z., Wang, Z., Le, L., Zheng, H. S., Mishra, S., Perot, V., Zhang, Y., Mattapalli, A., Taly, A., Shang, J., et al. (2024) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 9

VeriGuard: Enhancing LLM Agent Safety via Verified Code Generation

Miculicich, L., Parmar, M., Palangi, H., Dvijotham, K. D., Montanari, M., Pfister, T., & Le, L. T. (2025) · arXiv

Formal Methods in VerificationSafety Systems Engineering in Autonomy

Cited by 2

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