Schwartz, Roy

Green AI

Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2020) · Communications of the ACM

Green IT and SustainabilityEnergy Efficiency in Computing

Cited by 1,647

Annotation Artifacts in Natural Language Inference Data

Gururangan, S., Swayamdipta, S., Levy, O., Schwartz, R., Bowman, S., & Smith, N. A. (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 TechniquesTopic Modeling

Cited by 907

Knowledge Enhanced Contextual Word Representations

Peters, M. E., Neumann, M., Logan, R., Schwartz, R., Joshi, V., Singh, S., & Smith, N. A. (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)

Topic ModelingNatural Language Processing Techniques

Cited by 661

Fine-Tuning Pretrained Language Models: Weight Initializations, Data Orders, and Early Stopping

Dodge, J., Ilharco, G., Schwartz, R., Farhadi, A., Hajishirzi, H., & Smith, N. (2020) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 412

A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications

Kang, D., Ammar, W., Dalvi, B., Zuylen, M., Kohlmeier, S., Hovy, E., & Schwartz, R. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Sentiment Analysis and Opinion MiningNatural Language Processing Techniques

Cited by 270

Measuring the Carbon Intensity of AI in Cloud Instances

Dodge, J., Prewitt, T., Tachet des Combes, R., Odmark, E., Schwartz, R., Strubell, E., Luccioni, A. S., Smith, N. A., DeCario, N., & Buchanan, W. (2022) · 2022 ACM Conference on Fairness Accountability and Transparency

Green IT and SustainabilityEnergy Efficiency in Computing

Cited by 257

Show Your Work: Improved Reporting of Experimental Results

Dodge, J., Gururangan, S., Card, D., Schwartz, R., & Smith, N. A. (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)

Academic Writing and PublishingScientific Computing and Data Management

Cited by 233

Data Contamination: From Memorization to Exploitation

Magar, I., & Schwartz, R. (2022) · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 88

Green AI

Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2019) · arXiv

Green IT and SustainabilityEnergy Efficiency in Computing

Cited by 73

Provable Limitations of Acquiring Meaning from Ungrounded Form: What Will Future Language Models Understand?

Merrill, W., Goldberg, Y., Schwartz, R., & Smith, N. A. (2021) · Transactions of the Association for Computational Linguistics

Syntax, Semantics, Linguistic VariationLogic, Reasoning, and Knowledge

Cited by 44

Context Length Alone Hurts LLM Performance Despite Perfect Retrieval

Du, Y., Tian, M., Ronanki, S., Rongali, S., Bodapati, S. B., Galstyan, A., Wells, A., Schwartz, R., Huerta, E. A., & Peng, H. (2025) · Findings of the Association for Computational Linguistics: EMNLP 2025

Topic ModelingInformation Retrieval and Search Behavior

Cited by 39

Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images

Bitton-Guetta, N., Bitton, Y., Hessel, J., Schmidt, L., Elovici, Y., Stanovsky, G., & Schwartz, R. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Multimodal Machine Learning ApplicationsGenerative Adversarial Networks and Image Synthesis

Cited by 37

A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications

Kang, D., Ammar, W., Dalvi, B., van Zuylen, M., Kohlmeier, S., Hovy, E., & Schwartz, R. (2018) · arXiv

Computational and Text Analysis MethodsAcademic Writing and Publishing

Cited by 28

Show Your Work: Improved Reporting of Experimental Results

Dodge, J., Gururangan, S., Card, D., Schwartz, R., & Smith, N. A. (2019) · arXiv

Machine Learning and Data ClassificationNatural Language Processing Techniques

Cited by 22

Transformers are Multi-State RNNs

Oren, M., Hassid, M., Yarden, N., Adi, Y., & Schwartz, R. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 16

What Can Natural Language Processing Do for Peer Review?

Kuznetsov, I., Afzal, O. M., Dercksen, K., Dycke, N., Goldberg, A., Hope, T., Hovy, D., Kummerfeld, J. K., Lauscher, A., Leyton-Brown, K., et al. (2024) · arXiv

Academic Writing and PublishingPublishing and Scholarly Communication

Cited by 6

The Larger the Better? Improved LLM Code-Generation via Budget Reallocation

Hassid, M., Remez, T., Gehring, J., Schwartz, R., & Adi, Y. (2024) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 6