Chang, Kai-Wei

Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

Bolukbasi, T., Chang, K.-W., Zou, J., Saligrama, V., & Kalai, A. (2016) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 1,615

VisualBERT: A Simple and Performant Baseline for Vision and Language

Li, L. H., Yatskar, M., Yin, D., Hsieh, C.-J., & Chang, K.-W. (2019) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 1,198

Men Also Like Shopping: Reducing Gender Bias Amplification using Corpus-level Constraints

Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Ethics and Social Impacts of AIAdvanced Neural Network Applications

Cited by 829

Gender bias in coreference resolution: Evaluation and debiasing methods

Zhao, J., Wang, T., Yatskar, M., Ordonez, V., & Chang, K.-W. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Natural Language Processing TechniquesEthics and Social Impacts of AI

Cited by 684

Unified Pre-training for Program Understanding and Generation

Ahmad, W., Chakraborty, S., Ray, B., & Chang, K.-W. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Software Engineering ResearchTopic Modeling

Cited by 642

The Woman Worked as a Babysitter: On Biases in Language Generation

Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (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)

Ethics and Social Impacts of AINatural Language Processing Techniques

Cited by 537

Mitigating Gender Bias in Natural Language Processing: Literature Review

Sun, T., Gaut, A., Tang, S., Huang, Y., ElSherief, M., Zhao, J., Mirza, D., Belding, E., Chang, K.-W., & Wang, W. Y. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Ethics and Social Impacts of AIGender Studies in Language

Cited by 492

"Kelly is a Warm Person, Joseph is a Role Model": Gender Biases in LLM-Generated Reference Letters

Wan, Y., Pu, G., Sun, J., Garimella, A., Chang, K.-W., & Peng, N. (2023) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 175

Retrieval Augmented Code Generation and Summarization

Parvez, M. R., Ahmad, W., Chakraborty, S., Ray, B., & Chang, K.-W. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021

Software Engineering ResearchTopic Modeling

Cited by 141

Societal Biases in Language Generation: Progress and Challenges

Sheng, E., Chang, K.-W., Natarajan, P., & Peng, N. (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)

Ethics and Social Impacts of AINatural Language Processing Techniques

Cited by 131

Towards a holistic framework for multimodal LLM in 3D brain CT radiology report generation

Li, C.-Y., Chang, K.-J., Yang, C.-F., Wu, H.-Y., Chen, W., Bansal, H., Chen, L., Yang, Y.-P., Chen, Y.-C., Chen, S.-P., et al. (2025) · Nature Communications

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 75

On the Paradox of Learning to Reason from Data

Zhang, H., Li, L. H., Meng, T., Chang, K.-W., & Broeck, G. V. D. (2022) · arXiv

Topic ModelingLogic, Reasoning, and Knowledge

Cited by 58

On the Paradox of Learning to Reason from Data

Zhang, H., Li, L. H., Meng, T., Chang, K.-W., & Van den Broeck, G. (2023) · Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence

Topic ModelingLogic, Reasoning, and Knowledge

Cited by 58

On Prompt-Driven Safeguarding for Large Language Models

Zheng, C., Yin, F., Zhou, H., Meng, F., Zhou, J., Chang, K.-W., Huang, M., & Peng, N. (2024) · arXiv

Adversarial Robustness in Machine LearningHate Speech and Cyberbullying Detection

Cited by 46

AVATAR: A Parallel Corpus for Java-Python Program Translation

Ahmad, W. U., Tushar, M. G. R., Chakraborty, S., & Chang, K.-W. (2021) · arXiv

Software Engineering ResearchNatural Language Processing Techniques

Cited by 46

AVATAR: A Parallel Corpus for Java-Python Program Translation

Ahmad, W. U., Tushar, M. G. R., Chakraborty, S., & Chang, K.-W. (2023) · Findings of the Association for Computational Linguistics: ACL 2023

Software Engineering ResearchNatural Language Processing Techniques

Cited by 46

Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning

Lu, P., Qiu, L., Chang, K.-W., Wu, Y. N., Zhu, S.-C., Rajpurohit, T., Clark, P., & Kalyan, A. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 45

“I’m fully who I am”: Towards Centering Transgender and Non-Binary Voices to Measure Biases in Open Language Generation

Ovalle, A., Goyal, P., Dhamala, J., Jaggers, Z., Chang, K.-W., Galstyan, A., Zemel, R., & Gupta, R. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency

Ethics and Social Impacts of AILGBTQ Health, Identity, and Policy

Cited by 43

Red Teaming Language Model Detectors with Language Models

Shi, Z., Wang, Y., Yin, F., Chen, X., Chang, K.-W., & Hsieh, C.-J. (2023) · arXiv

Adversarial Robustness in Machine LearningAuthorship Attribution and Profiling

Cited by 30

3DLLM-Mem: Long-Term Spatial-Temporal Memory for Embodied 3D Large Language Model

Hu, W., Hong, Y., Wang, Y., Gao, L., Wei, Z., Yao, X., Peng, N., Bitton, Y., Szpektor, I., & Chang, K.-W. (2025) · Advances in Neural Information Processing Systems 38

Robot Manipulation and LearningMultimodal Machine Learning Applications

Cited by 27

Agent Lumos: Unified and Modular Training for Open-Source Language Agents

Yin, D., Brahman, F., Ravichander, A., Chandu, K., Chang, K.-W., Choi, Y., & Lin, B. Y. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Natural Language Processing TechniquesSpeech and dialogue systems

Cited by 17

Model Editing Harms General Abilities of Large Language Models: Regularization to the Rescue

Gu, J.-C., Xu, H.-X., Ma, J.-Y., Lu, P., Ling, Z.-H., Chang, K.-W., & Peng, N. (2024) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 16

SpeechPrompt v2: Prompt Tuning for Speech Classification Tasks

Chang, K.-W., Wang, Y.-K., Shen, H., Kang, I.-T., Tseng, W.-C., Li, S.-W., & Lee, H.-Y. (2023) · arXiv

Speech Recognition and SynthesisDomain Adaptation and Few-Shot Learning

Cited by 15

Watermarking Pre-trained Language Models with Backdooring

Gu, C., Huang, C., Zheng, X., Chang, K.-W., & Hsieh, C.-J. (2022) · arXiv

Adversarial Robustness in Machine LearningAdvanced Steganography and Watermarking Techniques

Cited by 15