Tsvetkov, Yulia

Measuring Bias in Contextualized Word Representations

Kurita, K., Vyas, N., Pareek, A., Black, A. W., & Tsvetkov, Y. (2019) · Proceedings of the First Workshop on Gender Bias in Natural Language Processing

Ethics and Social Impacts of AITopic Modeling

Cited by 348

Style transfer through back-translation

Prabhumoye, S., Tsvetkov, Y., Salakhutdinov, R., & Black, A. W. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Generative Adversarial Networks and Image SynthesisAuthorship Attribution and Profiling

Cited by 348

Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics

Pagnoni, A., Balachandran, V., & Tsvetkov, Y. (2021) · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Topic ModelingAdvanced Text Analysis Techniques

Cited by 197

From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models

Feng, S., Park, C. Y., Liu, Y., & Tsvetkov, Y. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Hate Speech and Cyberbullying DetectionEthics and Social Impacts of AI

Cited by 149

Stress and burnout in open source: toward finding, understanding, and mitigating unhealthy interactions

Raman, N., Cao, M., Tsvetkov, Y., Kästner, C., & Vasilescu, B. (2020) · Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering: New Ideas and Emerging Results

Open Source Software InnovationsSoftware Engineering Research

Cited by 70

Quantifying Language Models' Sensitivity to Spurious Features in Prompt Design or: How I learned to start worrying about prompt formatting

Sclar, M., Choi, Y., Tsvetkov, Y., & Suhr, A. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 52

Fine-grained Hallucination Detection and Editing for Language Models

Mishra, A., Asai, A., Balachandran, V., Wang, Y., Neubig, G., Tsvetkov, Y., & Hajishirzi, H. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 49

Don’t Hallucinate, Abstain: Identifying LLM Knowledge Gaps via Multi-LLM Collaboration

Feng, S., Shi, W., Wang, Y., Ding, W., Balachandran, V., & Tsvetkov, Y. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 40

Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey

Kumar, S., Balachandran, V., Njoo, L., Anastasopoulos, A., & Tsvetkov, Y. (2023) · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Ethics and Social Impacts of AIHate Speech and Cyberbullying Detection

Cited by 38

Gendered Mental Health Stigma in Masked Language Models

Lin, I., Njoo, L., Field, A., Sharma, A., Reinecke, K., Althoff, T., & Tsvetkov, Y. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Ethics and Social Impacts of AIMental Health Treatment and Access

Cited by 33

Minding Language Models’ (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker

Sclar, M., Kumar, S., West, P., Suhr, A., Choi, Y., & Tsvetkov, Y. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Child and Animal Learning DevelopmentLogic, Reasoning, and Knowledge

Cited by 27

Model Swarms: Collaborative Search to Adapt LLM Experts via Swarm Intelligence

Feng, S., Wang, Z., Wang, Y., Ebrahimi, S., Palangi, H., Miculicich, L., Kulshrestha, A., Rauschmayr, N., Choi, Y., Tsvetkov, Y., et al. (2024) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 24

A Computational Analysis of Polarization on Indian and Pakistani Social Media

Tyagi, A., Field, A., Lathwal, P., Tsvetkov, Y., & Carley, K. M. (2020) · Lecture Notes in Computer Science

Social Media and PoliticsMisinformation and Its Impacts

Cited by 20

MediQ: Question-Asking LLMs and a Benchmark for Reliable Interactive Clinical Reasoning

Li, S., Balachandran, V., Feng, S., Ilgen, J., Pierson, E., Koh, P., & Tsvetkov, Y. (2024) · Advances in Neural Information Processing Systems 37

Artificial Intelligence in Healthcare and EducationMultimodal Machine Learning Applications

Cited by 19

Examining risks of racial biases in NLP tools for child protective services

Field, A., Coston, A., Gandhi, N., Chouldechova, A., Putnam-Hornstein, E., Steier, D., & Tsvetkov, Y. (2023) · 2023 ACM Conference on Fairness, Accountability, and Transparency

Ethics and Social Impacts of AIArtificial Intelligence in Law

Cited by 18

Modular Pluralism: Pluralistic Alignment via Multi-LLM Collaboration

Feng, S., Sorensen, T., Liu, Y., Fisher, J., Park, C. Y., Choi, Y., & Tsvetkov, Y. (2024) · arXiv

Ethics and Social Impacts of AITopic Modeling

Cited by 18

Heterogeneous Swarms: Jointly Optimizing Model Roles and Weights for Multi-LLM Systems

Feng, S., Wang, Z., Goyal, P., Wang, Y., Shi, W., Xia, H., Palangi, H., Zettlemoyer, L., Tsvetkov, Y., Lee, C.-Y., et al. (2025) · arXiv

Topic ModelingMetaheuristic Optimization Algorithms Research

Cited by 18

Do Membership Inference Attacks Work on Large Language Models?

Duan, M., Suri, A., Mireshghallah, N., Min, S., Shi, W., Zettlemoyer, L., Tsvetkov, Y., Choi, Y., Evans, D., & Hajishirzi, H. (2024) · arXiv

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 17

Can LLMs Keep a Secret? Testing Privacy Implications of Language Models via Contextual Integrity Theory

Mireshghallah, N., Kim, H., Zhou, X., Tsvetkov, Y., Sap, M., Shokri, R., & Choi, Y. (2023) · arXiv

Privacy-Preserving Technologies in DataPrivacy, Security, and Data Protection

Cited by 14

BUFFET: Benchmarking Large Language Models for Few-shot Cross-lingual Transfer

Asai, A., Kudugunta, S., Yu, X. V., Blevins, T., Gonen, H., Reid, M., Tsvetkov, Y., Ruder, S., & Hajishirzi, H. (2023) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 10

Trusting Your Evidence: Hallucinate Less with Context-aware Decoding

Shi, W., Han, X., Lewis, M., Tsvetkov, Y., Zettlemoyer, L., & Yih, S. W.-T. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 6

SemStamp: A semantic watermark with paraphrastic robustness for text generation

Hou, A. B., Zhang, J., He, T., Wang, Y., Chuang, Y.-S., Wang, H., Shen, L., Van Durme, B., Khashabi, D., & Tsvetkov, Y. (2023) · arXiv

Advanced Steganography and Watermarking TechniquesAdversarial Robustness in Machine Learning

Cited by 3

Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks

Wang, Y., Feng, S., Hou, A. B., Pu, X., Shen, C., Liu, X., Tsvetkov, Y., & He, T. (2024) · arXiv

Adversarial Robustness in Machine LearningAuthorship Attribution and Profiling

Cited by 2

Spurious Rewards: Rethinking Training Signals in RLVR

Shao, R., Li, S. S., Xin, R., Geng, S., Wang, Y., Oh, S., Du, S. S., Lambert, N., Min, S., Krishna, R., et al. (2025) · arXiv

Reinforcement Learning in RoboticsTopic Modeling

Cited by 1