Qin, Guanghui

Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

Qin, G., & Eisner, J. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 342

The NLP Task Effectiveness of Long-Range Transformers

Qin, G., Feng, Y., & Van Durme, B. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 14

Researchy Questions: A Dataset of Multi-Perspective, Decompositional Questions for LLM Web Agents

Rosset, C., Chung, H.-L., Qin, G., Chau, E. C., Feng, Z., Awadallah, A., Neville, J., & Rao, N. (2024) · arXiv

Information Retrieval and Search BehaviorTopic Modeling

Cited by 6

The Illusion of Readiness in Health AI

Gu, Y., Fu, J., Liu, X., Valanarasu, J. M. J., Codella, N. C., Tan, R., Liu, Q., Jin, Y., Zhang, S., Wang, J., et al. (2025) · arXiv

Artificial Intelligence in Healthcare and EducationAdversarial Robustness in Machine Learning

Cited by 1

KV-Distill: Nearly Lossless Learnable Context Compression for LLMs

Chari, V., Qin, G., & Van Durme, B. (2025) · arXiv

Topic ModelingAdvanced Neural Network Applications

Cited by 0