Liu, Zihan

Multimodal End-to-End Sparse Model for Emotion Recognition

Dai, W., Cahyawijaya, S., Liu, Z., & Fung, P. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Emotion and Mood RecognitionSentiment Analysis and Opinion Mining

Cited by 91

Nemotron-4 340B Technical Report

Nvidia, :, Adler, B., Agarwal, N., Aithal, A., Anh, D. H., Bhattacharya, P., Brundyn, A., Casper, J., Catanzaro, B., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 66

RankRAG: Unifying Context Ranking with Retrieval-Augmented Generation in LLMs

Yu, Y., Ping, W., Liu, Z., Wang, B., You, J., Zhang, C., Shoeybi, M., & Catanzaro, B. (2024) · Advances in Neural Information Processing Systems 37

Topic ModelingInformation Retrieval and Search Behavior

Cited by 47

RHO: Reducing Hallucination in Open-domain Dialogues with Knowledge Grounding

Ji, Z., Liu, Z., Lee, N., Yu, T., Wilie, B., Zeng, M., & Fung, P. (2023) · Findings of the Association for Computational Linguistics: ACL 2023

Speech and dialogue systemsTopic Modeling

Cited by 37

Language Models as Few-Shot Learner for Task-Oriented Dialogue Systems

Madotto, A., Liu, Z., Lin, Z., & Fung, P. (2020) · arXiv

Speech and dialogue systemsDomain Adaptation and Few-Shot Learning

Cited by 36

Shall We Pretrain Autoregressive Language Models with Retrieval? A Comprehensive Study

Wang, B., Ping, W., Xu, P., McAfee, L., Liu, Z., Shoeybi, M., Dong, Y., Kuchaiev, O., Li, B., Xiao, C., et al. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingInformation Retrieval and Search Behavior

Cited by 21

Retrieval meets Long Context Large Language Models

Xu, P., Ping, W., Wu, X., McAfee, L., Zhu, C., Liu, Z., Subramanian, S., Bakhturina, E., Shoeybi, M., & Catanzaro, B. (2023) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 20

Retrieval-Free Knowledge-Grounded Dialogue Response Generation with Adapters

Xu, Y., Ishii, E., Cahyawijaya, S., Liu, Z., Winata, G. I., Madotto, A., Su, D., & Fung, P. (2021) · arXiv

Speech and dialogue systemsTopic Modeling

Cited by 17

ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities

Xu, P., Ping, W., Wu, X., Xu, C., Liu, Z., Shoeybi, M., & Catanzaro, B. (2024) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 13

AceMath: Advancing Frontier Math Reasoning with Post-Training and Reward Modeling

Liu, Z., Chen, Y., Shoeybi, M., Catanzaro, B., & Ping, W. (2024) · arXiv

Topic ModelingMathematics, Computing, and Information Processing

Cited by 5

NVLM: Open Frontier-Class Multimodal LLMs

Dai, W., Lee, N., Wang, B., Yang, Z., Liu, Z., Barker, J., Rintamaki, T., Shoeybi, M., Catanzaro, B., & Ping, W. (2024) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 3