He, Luheng

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

End-to-end Neural Coreference Resolution

Lee, K., He, L., Lewis, M., & Zettlemoyer, L. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 869

Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Construction

Luan, Y., He, L., Ostendorf, M., & Hajishirzi, H. (2018) · arXiv

Topic ModelingBiomedical Text Mining and Ontologies

Cited by 714

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context

Gemini Team, Georgiev, P., Lei, V. I., Burnell, R., Bai, L., Gulati, A., Tanzer, G., Vincent, D., Pan, Z., Wang, S., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 306

Capabilities of Gemini Models in Medicine

Saab, K., Tu, T., Weng, W.-H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., Strother, T., Park, C., Vedadi, E., et al. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 117

TableFormer: Robust Transformer Modeling for Table-Text Encoding

Yang, J., Gupta, A., Upadhyay, S., He, L., Goel, R., & Paul, S. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingMultimodal Machine Learning Applications

Cited by 85

Giving BERT a Calculator: Finding Operations and Arguments with Reading Comprehension

Andor, D., He, L., Lee, K., & Pitler, E. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 72

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

TIMEDIAL: Temporal Commonsense Reasoning in Dialog

Qin, L., Gupta, A., Upadhyay, S., He, L., Choi, Y., & Faruqui, M. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Speech and dialogue systemsNatural Language Processing Techniques

Cited by 59

A General Framework for Information Extraction using Dynamic Span Graphs

Luan, Y., Wadden, D., He, L., Shah, A., Ostendorf, M., & Hajishirzi, H. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 34