Su, Hongjin

Selective annotation makes language models better few-shot learners

Su, H., Kasai, J., Wu, C. H., Shi, W., Wang, T., Xin, J., Zhang, R., Ostendorf, M., Zettlemoyer, L., Smith, N. A., et al. (2022) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 70

Generative Representational Instruction Tuning

Muennighoff, N., Su, H., Wang, L., Yang, N., Wei, F., Yu, T., Singh, A., & Kiela, D. (2024) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 16

EvoR: Evolving Retrieval for Code Generation

Su, H., Jiang, S., Lai, Y., Wu, H., Shi, B., Liu, C., Liu, Q., & Yu, T. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024

Software Engineering ResearchTopic Modeling

Cited by 12

Spider 2.0: Evaluating Language Models on Real-World Enterprise Text-to-SQL Workflows

Lei, F., Chen, J., Ye, Y., Cao, R., Shin, D., Su, H., Suo, Z., Gao, H., Hu, W., Yin, P., et al. (2024) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 7

Learn-by-interact: A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments

Su, H., Sun, R., Yoon, J., Yin, P., Yu, T., & Arık, S. Ö. (2025) · arXiv

Topic ModelingSoftware Engineering Research

Cited by 2