Sui, Zhifang

A Survey on In-context Learning

Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Chang, B., et al. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 649

DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Dai, D., Deng, C., Zhao, C., Xu, R. X., Gao, H., Chen, D., Li, J., Zeng, W., Yu, X., Wu, Y., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 291

A Survey on In-context Learning

Dong, Q., Li, L., Dai, D., Zheng, C., Ma, J., Li, R., Xia, H., Xu, J., Wu, Z., Liu, T., et al. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 248

Knowledge Neurons in Pretrained Transformers

Dai, D., Dong, L., Hao, Y., Sui, Z., Chang, B., & Wei, F. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingExplainable Artificial Intelligence (XAI)

Cited by 187

Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers

Dai, D., Sun, Y., Dong, L., Hao, Y., Ma, S., Sui, Z., & Wei, F. (2023) · Findings of the Association for Computational Linguistics: ACL 2023

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 154

Large Language Models are not Fair Evaluators

Wang, P., Li, L., Chen, L., Cai, Z., Zhu, D., Lin, B., Cao, Y., Kong, L., Liu, Q., Liu, T., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Ethics and Social Impacts of AITopic Modeling

Cited by 138

XGPT: Cross-modal Generative Pre-Training for Image Captioning

Xia, Q., Huang, H., Duan, N., Zhang, D., Ji, L., Sui, Z., Cui, E., Bharti, T., & Zhou, M. (2021) · Lecture Notes in Computer Science

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 70

A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation

Liu, T., Zhang, Y., Brockett, C., Mao, Y., Sui, Z., Chen, W., & Dolan, B. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 63

Speculative Decoding: Exploiting Speculative Execution for Accelerating Seq2seq Generation

Xia, H., Ge, T., Wang, P., Chen, S.-Q., Wei, F., & Sui, Z. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Natural Language Processing TechniquesTopic Modeling

Cited by 40

Calibrating Factual Knowledge in Pretrained Language Models

Dong, Q., Dai, D., Song, Y., Xu, J., Sui, Z., & Li, L. (2022) · Findings of the Association for Computational Linguistics: EMNLP 2022

Topic ModelingMultimodal Machine Learning Applications

Cited by 39

Knowledge Neurons in Pretrained Transformers

Dai, D., Dong, L., Hao, Y., Sui, Z., Chang, B., & Wei, F. (2021) · arXiv

Topic ModelingExplainable Artificial Intelligence (XAI)

Cited by 39

Large Language Models are not Fair Evaluators

Wang, P., Li, L., Chen, L., Cai, Z., Zhu, D., Lin, B., Cao, Y., Liu, Q., Liu, T., & Sui, Z. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 34

Why Can GPT Learn In-Context? Language Models Implicitly Perform Gradient Descent as Meta-Optimizers

Dai, D., Sun, Y., Dong, L., Hao, Y., Ma, S., Sui, Z., & Wei, F. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 31

Confidence v.s. Critique: A Decomposition of Self-Correction Capability for LLMs

Yang, Z., Zhang, Y., Wang, Y., Xu, Z., Lin, J., & Sui, Z. (2025) · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 22

Towards Faithfulness in Open Domain Table-to-text Generation from an Entity-centric View

Liu, T., Zheng, X., Chang, B., & Sui, Z. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence

Topic ModelingMultimodal Machine Learning Applications

Cited by 21

DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models

Dai, D., Deng, C., Zhao, C., Xu, R. X., Gao, H., Chen, D., Li, J., Zeng, W., Yu, X., Wu, Y., et al. (2024) · arXiv

Topic ModelingAdvanced Neural Network Applications

Cited by 18

A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation

Liu, T., Zhang, Y., Brockett, C., Mao, Y., Sui, Z., Chen, W., & Dolan, B. (2021) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 12

Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

Wang, P., Li, L., Shao, Z., Xu, R. X., Dai, D., Li, Y., Chen, D., Wu, Y., & Sui, Z. (2023) · arXiv

Topic ModelingMathematics, Computing, and Information Processing

Cited by 3

Self-Boosting Large Language Models with Synthetic Preference Data

Dong, Q., Dong, L., Zhang, X., Sui, Z., & Wei, F. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1