Jurafsky, Dan

Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset

Henderson, P., Krass, M. S., Zheng, L., Guha, N., Manning, C. D., Jurafsky, D., & Ho, D. E. (2022) · arXiv

Artificial Intelligence in LawEthics and Social Impacts of AI

Cited by 49

Measuring machine translation quality as semantic equivalence: A metric based on entailment features

Padó, S., Cer, D., Galley, M., Jurafsky, D., & Manning, C. D. (2009) · Machine Translation

Natural Language Processing TechniquesTopic Modeling

Cited by 47

Navigating the Grey Area: How Expressions of Uncertainty and Overconfidence Affect Language Models

Zhou, K., Jurafsky, D., & Hashimoto, T. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 38

Dialect prejudice predicts AI decisions about people's character, employability, and criminality

Hofmann, V., Kalluri, P. R., Jurafsky, D., & King, S. (2024) · arXiv

Ethics and Social Impacts of AILinguistic Variation and Morphology

Cited by 33

When Do Pre-Training Biases Propagate to Downstream Tasks? A Case Study in Text Summarization

Ladhak, F., Durmus, E., Suzgun, M., Zhang, T., Jurafsky, D., McKeown, K., & Hashimoto, T. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 30

KTO: Model Alignment as Prospect Theoretic Optimization

Ethayarajh, K., Xu, W., Muennighoff, N., Jurafsky, D., & Kiela, D. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 29

Sycophantic AI Decreases Prosocial Intentions and Promotes Dependence

Cheng, M., Lee, C., Khadpe, P., Yu, S., Han, D., & Jurafsky, D. (2025) · arXiv

Ethics and Social Impacts of AISocial Robot Interaction and HRI

Cited by 17

ELEPHANT: Measuring and understanding social sycophancy in LLMs

Cheng, M., Yu, S., Lee, C., Khadpe, P., Ibrahim, L., & Jurafsky, D. (2025) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 12

The Authenticity Gap in Human Evaluation

Ethayarajh, K., & Jurafsky, D. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 10

Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory

Suzgun, M., Yuksekgonul, M., Bianchi, F., Jurafsky, D., & Zou, J. (2026) · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

Domain Adaptation and Few-Shot LearningNatural Language Processing Techniques

Cited by 9

Belief in the Machine: Investigating Epistemological Blind Spots of Language Models

Suzgun, M., Gur, T., Bianchi, F., Ho, D. E., Icard, T., Jurafsky, D., & Zou, J. (2024) · arXiv

Logic, Reasoning, and KnowledgeTopic Modeling

Cited by 3

Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory

Suzgun, M., Yuksekgonul, M., Bianchi, F., Jurafsky, D., & Zou, J. (2025) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 0

AxBench: Steering LLMs? Even Simple Baselines Outperform Sparse Autoencoders

Wu, Z., Arora, A., Geiger, A., Wang, Z., Huang, J., Jurafsky, D., Manning, C. D., & Potts, C. (2025) · arXiv

Explainable Artificial Intelligence (XAI)Topic Modeling

Cited by 0

Accommodation and Epistemic Vigilance: A Pragmatic Account of Why LLMs Fail to Challenge Harmful Beliefs

Cheng, M., Hawkins, R. D., & Jurafsky, D. (2026) · arXiv

Ethics and Social Impacts of AIMisinformation and Its Impacts

Cited by 0