Wieting, John

Gemma 3 Technical Report

Gemma Team, Kamath, A., Ferret, J., Pathak, S., Vieillard, N., Merhej, R., Perrin, S., Matejovicova, T., Ramé, A., Rivière, M., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 1,156

Gemini: A Family of Highly Capable Multimodal Models

Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K., et al. (2023) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 990

PaLM 2 Technical Report

Anil, R., Dai, A. M., Firat, O., Johnson, M., Lepikhin, D., Passos, A., Shakeri, S., Taropa, E., Bailey, P., Chen, Z., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 834

Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense

Krishna, K., Song, Y., Karpinska, M., Wieting, J., & Iyyer, M. (2023) · arXiv

Authorship Attribution and ProfilingSpam and Phishing Detection

Cited by 150

Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense

Krishna, K., Song, Y., Karpinska, M., Wieting, J., & Iyyer, M. (2023) · Advances in Neural Information Processing Systems 36

Authorship Attribution and ProfilingSpam and Phishing Detection

Cited by 150

Gemma 4 Technical Report

Gemma Team, Abd, S. E., Aggarwal, V., Algayres, R., Andreev, A., Bachem, O., Ballantyne, I., Brick, C., Cărbune, V., Casbon, M., et al. (2026) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 112

Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities

Comanici, G., Bieber, E., Schaekermann, M., Pasupat, I., Sachdeva, N., Dhillon, I., Blistein, M., Ram, O., Zhang, D., Rosen, E., et al. (2025) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 69

RankGen: Improving Text Generation with Large Ranking Models

Krishna, K., Chang, Y., Wieting, J., & Iyyer, M. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 30

Evaluating Large Language Models on Controlled Generation Tasks

Sun, J., Tian, Y., Zhou, W., Xu, N., Hu, Q., Gupta, R., Wieting, J., Peng, N., & Ma, X. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 29

Paraphrastic Representations at Scale

Wieting, J., Gimpel, K., Neubig, G., & Berg-kirkpatrick, T. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Topic ModelingNatural Language Processing Techniques

Cited by 13

StoryScope: Investigating idiosyncrasies in AI fiction

Russell, J., Rajendhran, R., Pham, C. M., Iyyer, M., & Wieting, J. (2026) · arXiv

Authorship Attribution and ProfilingArtificial Intelligence in Games

Cited by 13

Evaluating Large Language Models on Controlled Generation Tasks

Sun, J., Tian, Y., Zhou, W., Xu, N., Hu, Q., Gupta, R., Wieting, J. F., Peng, N., & Ma, X. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1

POLARIS: Guiding Small Models to Write Long Stories

Rajendhran, R., Russell, J., Iyyer, M., & Wieting, J. F. (2026) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 0