Klein, Dan

Learning Accurate, Compact, and Interpretable Tree Annotation

Petrov, S., Barrett, L., Thibaux, R., & Klein, D. (2006) · Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL - ACL '06

Natural Language Processing TechniquesSyntax, Semantics, Linguistic Variation

Cited by 793

Why Do Multi-Agent LLM Systems Fail?

Cemri, M., Pan, M. Z., Yang, S., Agrawal, L. A., Chopra, B., Tiwari, R., Keutzer, K., Parameswaran, A., Klein, D., Ramchandran, K., et al. (2025) · arXiv

Multi-Agent Systems and NegotiationNatural Language Processing Techniques

Cited by 630

Calibrate Before Use: Improving Few-Shot Performance of Language Models

Zhao, T. Z., Wallace, E., Feng, S., Klein, D., & Singh, S. (2021) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 513

Speaker-Follower Models for Vision-and-Language Navigation

Fried, D., Hu, R., Cirik, V., Rohrbach, A., Andreas, J., Morency, L.-P., Berg-Kirkpatrick, T., Saenko, K., Klein, D., & Darrell, T. (2018) · arXiv

Multimodal Machine Learning ApplicationsReinforcement Learning in Robotics

Cited by 291

Modular Multitask Reinforcement Learning with Policy Sketches

Andreas, J., Klein, D., & Levine, S. (2016) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 274

Constrained Language Models Yield Few-Shot Semantic Parsers

Shin, R., Lin, C., Thomson, S., Chen, C., Roy, S., Platanios, E. A., Pauls, A., Klein, D., Eisner, J., & Van Durme, B. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 139

Semantic Evaluation for Text-to-SQL with Distilled Test Suites

Zhong, R., Yu, T., & Klein, D. (2020) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 124

Reasoning about Pragmatics with Neural Listeners and Speakers

Andreas, J., & Klein, D. (2016) · Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing

Multimodal Machine Learning ApplicationsSpeech and dialogue systems

Cited by 115

Semantic Evaluation for Text-to-SQL with Distilled Test Suites

Zhong, R., Yu, T., & Klein, D. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 112

Task-Oriented Dialogue as Dataflow Synthesis

Andreas, J., Bufe, J., Burkett, D., Chen, C., Clausman, J., Crawford, J., Crim, K., DeLoach, J., Dorner, L., Eisner, J., et al. (2020) · Transactions of the Association for Computational Linguistics

Speech and dialogue systemsNatural Language Processing Techniques

Cited by 98

Re3: Generating Longer Stories With Recursive Reprompting and Revision

Yang, K., Tian, Y., Peng, N., & Klein, D. (2022) · arXiv

Artificial Intelligence in GamesTopic Modeling

Cited by 91

Linguistic Bias in ChatGPT: Language Models Reinforce Dialect Discrimination

Fleisig, E., Smith, G., Bossi, M., Rustagi, I., Yin, X., & Klein, D. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Ethics and Social Impacts of AILinguistic Variation and Morphology

Cited by 64

Unified Pragmatic Models for Generating and Following Instructions

Fried, D., Andreas, J., & Klein, D. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)

Speech and dialogue systemsNatural Language Processing Techniques

Cited by 64

Ghostbuster: Detecting Text Ghostwritten by Large Language Models

Verma, V., Fleisig, E., Tomlin, N., & Klein, D. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Authorship Attribution and ProfilingNatural Language Processing Techniques

Cited by 56

Discovering Latent Knowledge in Language Models Without Supervision

Burns, C., Ye, H., Klein, D., & Steinhardt, J. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 55

Poisoning Language Models During Instruction Tuning

Wan, A., Wallace, E., Shen, S., & Klein, D. (2023) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 42

Ghostbuster: Detecting Text Ghostwritten by Large Language Models

Verma, V., Fleisig, E., Tomlin, N., & Klein, D. (2023) · arXiv

Authorship Attribution and ProfilingDigital Media Forensic Detection

Cited by 16

What Evidence Do Language Models Find Convincing?

Wan, A., Wallace, E., & Klein, D. (2024) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 9

Learning by Distilling Context

Snell, C., Klein, D., & Zhong, R. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 8

GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning

Agrawal, L. A., Tan, S., Soylu, D., Ziems, N., Khare, R., Opsahl-Ong, K., Singhvi, A., Shandilya, H., Ryan, M. J., Jiang, M., et al. (2025) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 7

Goal Driven Discovery of Distributional Differences via Language Descriptions

Zhong, R., Zhang, P., Li, S., Ahn, J., Klein, D., & Steinhardt, J. (2023) · arXiv

Topic ModelingComputational and Text Analysis Methods

Cited by 4

Batching the Reflective Optimization Loop: Parallel Proposals Make GEPA Faster and Better

Zhang, J., Agrawal, L. A., Tan, S., Lee, D., Erdogan, L. E., Klein, D., Sen, K., Dimakis, A., & Zaharia, M. (2026) · arXiv

Evolutionary Algorithms and ApplicationsNatural Language Processing Techniques

Cited by 0