Liu, Ruibo

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

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

Gemma: Open Models Based on Gemini Research and Technology

Gemma Team, Mesnard, T., Hardin, C., Dadashi, R., Bhupatiraju, S., Pathak, S., Sifre, L., Rivière, M., Kale, M. S., Love, J., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 284

Quantifying and alleviating political bias in language models

Liu, R., Jia, C., Wei, J., Xu, G., & Vosoughi, S. (2022) · Artificial Intelligence

Ethics and Social Impacts of AIHate Speech and Cyberbullying Detection

Cited by 68

Long-form factuality in large language models

Wei, J., Yang, C., Song, X., Lu, Y., Hu, N., Huang, J., Tran, D., Peng, D., Liu, R., Huang, D., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 36

Mind's Eye: Grounded Language Model Reasoning through Simulation

Liu, R., Wei, J., Gu, S. S., Wu, T.-Y., Vosoughi, S., Cui, C., Zhou, D., & Dai, A. M. (2022) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 24

Best Practices and Lessons Learned on Synthetic Data

Liu, R., Wei, J., Liu, F., Si, C., Zhang, Y., Rao, J., Zheng, S., Peng, D., Yang, D., Zhou, D., et al. (2024) · arXiv

Topic ModelingEthics and Social Impacts of AI

Cited by 21

Training Socially Aligned Language Models on Simulated Social Interactions

Liu, R., Yang, R., Jia, C., Zhang, G., Zhou, D., Dai, A. M., Yang, D., & Vosoughi, S. (2023) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 18

Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineering

Si, C., Zhang, Y., Li, R., Yang, Z., Liu, R., & Yang, D. (2025) · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Software Engineering ResearchMultimodal Machine Learning Applications

Cited by 17

Design2Code: Benchmarking Multimodal Code Generation for Automated Front-End Engineering

Si, C., Zhang, Y., Li, R., Yang, Z., Liu, R., & Yang, D. (2024) · arXiv

Software Engineering ResearchMultimodal Machine Learning Applications

Cited by 4

CodeEditorBench: Evaluating Code Editing Capability of Large Language Models

Guo, J., Li, Z., Liu, X., Ma, K., Zheng, T., Yu, Z., Pan, D., LI, Y., Liu, R., Wang, Y., et al. (2024) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 1