Nakov, Preslav

On the Risk of Misinformation Pollution with Large Language Models

Pan, Y., Pan, L., Chen, W., Nakov, P., Kan, M.-Y., & Wang, W. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Misinformation and Its ImpactsAdversarial Robustness in Machine Learning

Cited by 210

Factuality challenges in the era of large language models and opportunities for fact-checking

Augenstein, I., Baldwin, T., Cha, M., Chakraborty, T., Ciampaglia, G. L., Corney, D., DiResta, R., Ferrara, E., Hale, S., Halevy, A., et al. (2024) · Nature Machine Intelligence

Misinformation and Its ImpactsTopic Modeling

Cited by 187

A Survey of Confidence Estimation and Calibration in Large Language Models . -

Geng, J., Cai, F., Wang, Y., Koeppl, H., Nakov, P., & Gurevych, I. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 88

Faking Fake News for Real Fake News Detection: Propaganda-loaded Training Data Generation

Huang, K.-H., McKeown, K., Nakov, P., Choi, Y., & Ji, H. (2022) · arXiv

Misinformation and Its ImpactsSpam and Phishing Detection

Cited by 46

Leaf: Multiple-Choice Question Generation

Vachev, K., Hardalov, M., Karadzhov, G., Georgiev, G., Koychev, I., & Nakov, P. (2022) · Lecture Notes in Computer Science

Natural Language Processing TechniquesTopic Modeling

Cited by 45

Adapting Fake News Detection to the Era of Large Language Models

Su, J., Cardie, C., & Nakov, P. (2024) · Findings of the Association for Computational Linguistics: NAACL 2024

Misinformation and Its ImpactsSpam and Phishing Detection

Cited by 42

Factuality Challenges in the Era of Large Language Models

Augenstein, I., Baldwin, T., Cha, M., Chakraborty, T., Ciampaglia, G. L., Corney, D., DiResta, R., Ferrara, E., Hale, S., Halevy, A., et al. (2023) · arXiv

Misinformation and Its ImpactsEthics and Social Impacts of AI

Cited by 32

Fact-checking the output of large language models via token-level uncertainty quantification

Fadeeva, E., Rubashevskii, A., Shelmanov, A., Petrakov, S., Li, H., Mubarak, H., Tsymbalov, E., Kuzmin, G., Panchenko, A., Baldwin, T., et al. (2024) · Findings of the Association for Computational Linguistics ACL 2024

Topic ModelingNatural Language Processing Techniques

Cited by 29

M4GT-Bench: Evaluation Benchmark for Black-Box Machine-Generated Text Detection

Wang, Y., Mansurov, J., Ivanov, P., Su, J., Shelmanov, A., Tsvigun, A., Mohammed Afzal, O., Mahmoud, T., Puccetti, G., Arnold, T., et al. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Authorship Attribution and ProfilingNatural Language Processing Techniques

Cited by 25

Benchmarking Uncertainty Quantification Methods for Large Language Models with LM-Polygraph

Vashurin, R., Fadeeva, E., Vazhentsev, A., Rvanova, L., Vasilev, D., Tsvigun, A., Petrakov, S., Xing, R., Sadallah, A., Grishchenkov, K., et al. (2025) · Transactions of the Association for Computational Linguistics

Adversarial Robustness in Machine LearningTopic Modeling

Cited by 21

Can We Spot the "Fake News" Before It Was Even Written?

Nakov, P. (2020) · arXiv

Misinformation and Its ImpactsMedia Influence and Politics

Cited by 15

Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs

Yun, S., Lin, H., Thushara, R., Bhat, M. Q., Wang, Y., Jiang, Z., Deng, M., Wang, J., Tao, T., Li, J., et al. (2024) · arXiv

Multimodal Machine Learning ApplicationsSoftware Engineering Research

Cited by 8

LLM-DetectAIve: a Tool for Fine-Grained Machine-Generated Text Detection

Abassy, M., Elozeiri, K., Aziz, A., Ta, M. N., Tomar, R. V., Adhikari, B., Ahmed, S. E. D., Wang, Y., Mohammed Afzal, O., Xie, Z., et al. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Authorship Attribution and ProfilingNatural Language Processing Techniques

Cited by 7

Factcheck-Bench: Fine-Grained Evaluation Benchmark for Automatic Fact-checkers

Wang, Y., Reddy, R. G., Mujahid, Z. M., Arora, A., Rubashevskii, A., Geng, J., Afzal, O. M., Pan, L., Borenstein, N., Pillai, A., et al. (2023) · arXiv

Misinformation and Its ImpactsTopic Modeling

Cited by 3

LLM360: Towards Fully Transparent Open-Source LLMs

Liu, Z., Qiao, A., Neiswanger, W., Wang, H., Tan, B., Tao, T., Li, J., Wang, Y., Sun, S., Pangarkar, O., et al. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 3

ArabicMMLU: Assessing Massive Multitask Language Understanding in Arabic

Koto, F., Li, H., Shatnawi, S., Doughman, J., Sadallah, A. B., Alraeesi, A., Almubarak, K., Alyafeai, Z., Sengupta, N., Shehata, S., et al. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 1

Exploring the Limitations of Detecting Machine-Generated Text

Doughman, J., Afzal, O. M., Toyin, H. O., Shehata, S., Nakov, P., & Talat, Z. (2024) · arXiv

Authorship Attribution and ProfilingDigital Media Forensic Detection

Cited by 0