Liu, Yixin

A comprehensive survey on pretrained foundation models: a history from BERT to ChatGPT

Zhou, C., Li, Q., Li, C., Yu, J., Liu, Y., Wang, G., Zhang, K., Ji, C., Yan, Q., He, L., et al. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 574

RefSum: Refactoring Neural Summarization

Liu, Y., Dou, Z.-Y., & Liu, P. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingAdvanced Text Analysis Techniques

Cited by 36

PREM: A Simple Yet Effective Approach for Node-Level Graph Anomaly Detection

Pan, J., Liu, Y., Zheng, Y., & Pan, S. (2023) · 2023 IEEE International Conference on Data Mining (ICDM)

Advanced Graph Neural NetworksAnomaly Detection Techniques and Applications

Cited by 32

M3SciQA: A Multi-Modal Multi-Document Scientific QA Benchmark for Evaluating Foundation Models

Li, C., Shangguan, Z., Zhao, Y., Li, D., Liu, Y., & Cohan, A. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 8

PHYSICS: Benchmarking Foundation Models on University-Level Physics Problem Solving

Feng, K., Zhao, Y., Liu, Y., Yang, T., Zhao, C., Sous, J., & Cohan, A. (2025) · arXiv

Topic ModelingEducational Methods and Outcomes

Cited by 3