Yang, Diyi

Is ChatGPT a General-Purpose Natural Language Processing Task Solver?

Qin, C., Zhang, A., Zhang, Z., Chen, J., Yasunaga, M., & Yang, D. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 869

Can Large Language Models Transform Computational Social Science?

Ziems, C., Held, W., Shaikh, O., Chen, J., Zhang, Z., & Yang, D. (2023) · arXiv

Computational and Text Analysis MethodsTopic Modeling

Cited by 839

Can Large Language Models Transform Computational Social Science?

Ziems, C., Held, W., Shaikh, O., Chen, J., Zhang, Z., & Yang, D. (2024) · Computational Linguistics

Computational and Text Analysis MethodsTopic Modeling

Cited by 585

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

Using large language models in psychology

Demszky, D., Yang, D., Yeager, D. S., Bryan, C. J., Clapper, M., Chandhok, S., Eichstaedt, J. C., Hecht, C., Jamieson, J., Johnson, M., et al. (2023) · Nature Reviews Psychology

Natural Language Processing TechniquesTopic Modeling

Cited by 369

ToTTo: A Controlled Table-To-Text Generation Dataset

Parikh, A., Wang, X., Gehrmann, S., Faruqui, M., Dhingra, B., Yang, D., & Das, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingMultimodal Machine Learning Applications

Cited by 252

ToTTo: A Controlled Table-To-Text Generation Dataset

Parikh, A. P., Wang, X., Gehrmann, S., Faruqui, M., Dhingra, B., Yang, D., & Das, D. (2020) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 252

Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond

Feder, A., Keith, K. A., Manzoor, E., Pryzant, R., Sridhar, D., Wood-Doughty, Z., Eisenstein, J., Grimmer, J., Reichart, R., Roberts, M. E., et al. (2022) · Transactions of the Association for Computational Linguistics

Advanced Causal Inference TechniquesComputational and Text Analysis Methods

Cited by 220

How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs

Zeng, Y., Lin, H., Zhang, J., Yang, D., Jia, R., & Shi, W. (2024) · arXiv

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 174

Evaluating the Effectiveness of Deplatforming as a Moderation Strategy on Twitter

Jhaver, S., Boylston, C., Yang, D., & Bruckman, A. (2021) · Proceedings of the ACM on Human-Computer Interaction

Hate Speech and Cyberbullying DetectionMisinformation and Its Impacts

Cited by 169

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Aremu, A., Bosselut, A., Chandu, K. R., Clinciu, M.-A., Das, D., Dhole, K., et al. (2021) · Proceedings of the 1st Workshop on Natural Language Generation, Evaluation, and Metrics (GEM 2021)

Natural Language Processing TechniquesTopic Modeling

Cited by 159

How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs

Zeng, Y., Lin, H., Zhang, J., Yang, D., Jia, R., & Shi, W. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Adversarial Robustness in Machine LearningEthics and Social Impacts of AI

Cited by 107

Will AI Console Me when I Lose my Pet? Understanding Perceptions of AI-Mediated Email Writing

Liu, Y., Mittal, A., Yang, D., & Bruckman, A. (2022) · CHI Conference on Human Factors in Computing Systems

AI in Service InteractionsSocial Robot Interaction and HRI

Cited by 92

Measure and Improve Robustness in NLP Models: A Survey

Wang, X., Wang, H., & Yang, D. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 90

ProgramBench: Can Language Models Rebuild Programs From Scratch?

Yang, J., Lieret, K., Ma, J., Thakkar, P., Pedchenko, D., Sootla, S., McMilin, E., Yin, P., Hou, R., Synnaeve, G., et al. (2026) · arXiv

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 84

Quantifying large language model usage in scientific papers

Liang, W., Zhang, Y., Wu, Z., Lepp, H., Ji, W., Zhao, X., Cao, H., Liu, S., He, S., Huang, Z., et al. (2025) · Nature Human Behaviour

scientometrics and bibliometrics researchAcademic Writing and Publishing

Cited by 80

On Second Thought, Let’s Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning

Shaikh, O., Zhang, H., Held, W., Bernstein, M., & Yang, D. (2022) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 76

On Second Thought, Let’s Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning

Shaikh, O., Zhang, H., Held, W., Bernstein, M., & Yang, D. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Ethics and Social Impacts of AITopic Modeling

Cited by 76

When FLUE Meets FLANG: Benchmarks and Large Pretrained Language Model for Financial Domain

Shah, R., Chawla, K., Eidnani, D., Shah, A., Du, W., Chava, S., Raman, N., Smiley, C., Chen, J., & Yang, D. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 74

Continual Learning for Text Classification with Information Disentanglement Based Regularization

Huang, Y., Zhang, Y., Chen, J., Wang, X., & Yang, D. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 74

Identifying and Mitigating the Security Risks of Generative AI

Barrett, C., Boyd, B., Bursztein, E., Carlini, N., Chen, B., Choi, J., Chowdhury, A. R., Christodorescu, M., Datta, A., Feizi, S., et al. (2023) · Foundations and Trends® in Privacy and Security

Adversarial Robustness in Machine LearningInformation and Cyber Security

Cited by 57

Mapping the Increasing Use of LLMs in Scientific Papers

Liang, W., Zhang, Y., Wu, Z., Lepp, H., Ji, W., Zhao, X., Cao, H., Liu, S., He, S., Huang, Z., et al. (2024) · arXiv

scientometrics and bibliometrics researchAcademic Writing and Publishing

Cited by 55

Unlearn What You Want to Forget: Efficient Unlearning for LLMs

Chen, J., & Yang, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Privacy-Preserving Technologies in DataDomain Adaptation and Few-Shot Learning

Cited by 52

The GEM Benchmark: Natural Language Generation, its Evaluation and Metrics

Gehrmann, S., Adewumi, T., Aggarwal, K., Ammanamanchi, P. S., Anuoluwapo, A., Bosselut, A., Chandu, K. R., Clinciu, M., Das, D., Dhole, K. D., et al. (2021) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 49