Chang, Baobao

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

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

An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models

Chen, L., Zhao, H., Liu, T., Bai, S., Lin, J., Zhou, C., & Chang, B. (2024) · arXiv

Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications

Cited by 159

MMICL: Empowering Vision-language Model with Multi-Modal In-Context Learning

Zhao, H., Cai, Z., Si, S., Ma, X., An, K., Chen, L., Liu, Z., Wang, S., Han, W., & Chang, B. (2023) · arXiv

Multimodal Machine Learning ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 120

Can We Edit Factual Knowledge by In-Context Learning?

Zheng, C., Li, L., Dong, Q., Fan, Y., Wu, Z., Xu, J., & Chang, B. (2023) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 54

Can We Edit Factual Knowledge by In-Context Learning?

Zheng, C., Li, L., Dong, Q., Fan, Y., Wu, Z., Xu, J., & Chang, B. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 54

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

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

ML-Bench: Evaluating Large Language Models and Agents for Machine Learning Tasks on Repository-Level Code

Tang, X., Liu, Y., Cai, Z., Shao, Y., Lu, J., Zhang, Y., Deng, Z., Hu, H., An, K., Huang, R., et al. (2023) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 2