Jurgens, David

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

Language from police body camera footage shows racial disparities in officer respect

Voigt, R., Camp, N. P., Prabhakaran, V., Hamilton, W. L., Hetey, R. C., Griffiths, C. M., Jurgens, D., Jurafsky, D., & Eberhardt, J. L. (2017) · Proceedings of the National Academy of Sciences

Policing Practices and PerceptionsLanguage, Discourse, Communication Strategies

Cited by 407

Measuring the predictability of life outcomes with a scientific mass collaboration

Salganik, M. J., Lundberg, I., Kindel, A. T., Ahearn, C. E., Al-Ghoneim, K., Almaatouq, A., Altschul, D. M., Brand, J. E., Carnegie, N. B., Compton, R. J., et al. (2020) · Proceedings of the National Academy of Sciences

Family Dynamics and RelationshipsOnline Learning and Analytics

Cited by 322

Measuring the Evolution of a Scientific Field through Citation Frames

Jurgens, D., Kumar, S., Hoover, R., McFarland, D., & Jurafsky, D. (2018) · Transactions of the Association for Computational Linguistics

scientometrics and bibliometrics researchWeb visibility and informetrics

Cited by 212

When "a helpful assistant" is not really helpful: Personas in system prompts do not improve performances of large language models

Zheng, M., Pei, J., Logeswaran, L., Lee, M., & Jurgens, D. (2024) · Findings of the Association for Computational Linguistics: EMNLP 2024

Topic ModelingNatural Language Processing Techniques

Cited by 82

Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark

Choi, M., Pei, J., Kumar, S., Shu, C., & Jurgens, D. (2023) · arXiv

Topic ModelingSentiment Analysis and Opinion Mining

Cited by 73

Do LLMs Understand Social Knowledge? Evaluating the Sociability of Large Language Models with SocKET Benchmark

Choi, M., Pei, J., Kumar, S., Shu, C., & Jurgens, D. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingSentiment Analysis and Opinion Mining

Cited by 38

Can Large Language Model Agents Simulate Human Trust Behavior?

Xie, C., Chen, C., Jia, F., Ye, Z., Lai, S., Shu, K., Gu, J., Bibi, A., Hu, Z., Jurgens, D., et al. (2024) · Advances in Neural Information Processing Systems 37

Social Robot Interaction and HRIMulti-Agent Systems and Negotiation

Cited by 31

MultiCite: Modeling realistic citations requires moving beyond the single-sentence single-label setting

Lauscher, A., Ko, B., Kuehl, B., Johnson, S., Jurgens, D., Cohan, A., & Lo, K. (2021) · arXiv

scientometrics and bibliometrics researchTopic Modeling

Cited by 3

Tokenization is Sensitive to Language Variation

Wegmann, A., Nguyen, D., & Jurgens, D. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0

NUTMEG: Separating Signal From Noise in Annotator Disagreement

Ivey, J., Gauch, S., & Jurgens, D. (2025) · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing

Machine Learning and Data ClassificationMobile Crowdsensing and Crowdsourcing

Cited by 0