Through the Looking Glass: Learning to Attribute Synthetic Text Generated by Language Models

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 48,293 · see citing works

Glove: Global Vectors for Word Representation

Pennington, J., Socher, R., & Manning, C. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1162 · Cited by 34,071 · see citing works

Language Models are Few-Shot Learners

Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372 · see citing works

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., & Zettlemoyer, L. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.703 · Cited by 8,821 · see citing works

XLNet: Generalized Autoregressive Pretraining for Language Understanding

Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1906.08237 · Cited by 6,687 · see citing works

Pointer Sentinel Mixture ModelsPreprint

Merity, S., Xiong, C., Bradbury, J., & Socher, R. (2016) · International Conference on Learning Representations · doi:10.48550/arxiv.1609.07843 · Cited by 5,051 · see citing works

DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019) · arXiv · doi:10.48550/arxiv.1910.01108 · Cited by 4,653 · see citing works

Universal Language Model Fine-tuning for Text Classification

Howard, J., & Ruder, S. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1031 · Cited by 4,100 · see citing works

Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures

Fredrikson, M., Jha, S., & Ristenpart, T. (2015) · Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security · doi:10.1145/2810103.2813677 · Cited by 2,961 · see citing works

Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books

Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., & Fidler, S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.11 · Cited by 2,264 · see citing works

How to Fine-Tune BERT for Text Classification?

Sun, C., Qiu, X., Xu, Y., & Huang, X. (2019) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-32381-3_16 · Cited by 1,534 · see citing works

The Curious Case of Neural Text Degeneration

Holtzman, A., Buys, J., Du, L., Forbes, M., & Choi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09751 · Cited by 1,304 · see citing works

Untitled work

(2020) · International Conference on Machine Learning · Cited by 1,012 · see citing works

Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity

Fedus, W., Zoph, B., & Shazeer, N. (2021) · arXiv · doi:10.48550/arxiv.2101.03961 · Cited by 1,005 · see citing works

Inference and disputed authorship : The Federalist

Mosteller, F. C., & Wallace, D. K. (1964) · Medical Entomology and Zoology · Cited by 916 · see citing works

Unsupervised Cross-Domain Image GenerationPreprint

Taigman, Y., Polyak, A., & Wolf, L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.02200 · Cited by 901 · see citing works

Pose Guided Person Image GenerationPreprint

Ma, L., Jia, X., Sun, Q., Schiele, B., Tuytelaars, T., & Gool, L. V. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.09368 · Cited by 708 · see citing works

CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training

Bao, J., Chen, D., Wen, F., Li, H., & Hua, G. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.299 · Cited by 594 · see citing works

Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints

Yu, N., Davis, L. S., & Fritz, M. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00765 · Cited by 491 · see citing works

GLTR: Statistical Detection and Visualization of Generated Text

Gehrmann, S., Strobelt, H., & Rush, A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · doi:10.18653/v1/p19-3019 · Cited by 470 · cited 5× · see citing works

Untitled work

(2020) · arXiv (Cornell University) · Cited by 461 · see citing works

Writeprints

Abbasi, A., & Chen, H. (2008) · ACM Transactions on Information Systems · doi:10.1145/1344411.1344413 · Cited by 405 · see citing works

Release Strategies and the Social Impacts of Language Models

Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., Radford, A., Krueger, G., Kim, J. W., Kreps, S., et al. (2019) · arXiv · doi:10.48550/arxiv.1908.09203 · Cited by 292 · see citing works

Automatic Detection of Generated Text is Easiest when Humans are Fooled

Ippolito, D., Duckworth, D., Callison-Burch, C., & Eck, D. (2019) · arXiv · doi:10.48550/arxiv.1911.00650 · Cited by 264 · cited 1×

Automatic Detection of Generated Text is Easiest when Humans are Fooled

Ippolito, D., Duckworth, D., Callison-Burch, C., & Eck, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.164 · Cited by 264 · see citing works

On the Feasibility of Internet-Scale Author Identification

Narayanan, A., Paskov, H., Gong, N. Z., Bethencourt, J., Stefanov, E., Shin, E. C. R., & Song, D. (2012) · 2012 IEEE Symposium on Security and Privacy · doi:10.1109/sp.2012.46 · Cited by 261 · see citing works

Privacy Risks of General-Purpose Language Models

Pan, X., Zhang, M., Ji, S., & Yang, M. (2020) · 2020 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp40000.2020.00095 · Cited by 188 · see citing works

The Cost of Training NLP Models: A Concise Overview

Sharir, O., Peleg, B., & Shoham, Y. (2020) · arXiv · doi:10.48550/arxiv.2004.08900 · Cited by 113 · see citing works

How to Fine-Tune BERT for Text Classification?Preprint

Sun, C. C., Qiu, X., Xu, Y., & Huang, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.05583 · Cited by 89 · see citing works

Character-level and Multi-channel Convolutional Neural Networks for Large-scale Authorship Attribution

Ruder, S., Ghaffari, P., & Breslin, J. G. (2016) · arXiv · doi:10.48550/arxiv.1609.06686 · Cited by 86 · see citing works

Real or Fake? Learning to Discriminate Machine from Human Generated Text

Bakhtin, A., Gross, S., Ott, M., Deng, Y., Ranzato, M., & Szlam, A. (2019) · arXiv · doi:10.48550/arxiv.1906.03351 · Cited by 77 · see citing works

Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT WorldsPreprint

Klein, T., & Nabi, M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.02365 · Cited by 58 · see citing works

Detecting authorship deception: a supervised machine learning approach using author writeprints

Pearl, L., & Steyvers, M. (2012) · Literary and Linguistic Computing · doi:10.1093/llc/fqs003 · Cited by 52 · see citing works

Evaluating Language Model Finetuning Techniques for Low-resource LanguagesPreprint

Cruz, J. C. B., & Cheng, C. K. (2019) · arXiv (Cornell University) · doi:10.13140/rg.2.2.23028.40322 · Cited by 43 · see citing works

A Comparative Study of Neural Network Models for Sentence Classification

Le-Hong, P., & Le, A.-C. (2018) · NAFOSTED Conference on Information and Computer Science (NICS) · doi:10.1109/nics.2018.8606879 · Cited by 18 · see citing works

A Girl Has A Name: Detecting Authorship Obfuscation

Mahmood, A., Shafiq, Z., & Srinivasan, P. (2020) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2020.acl-main.203 · Cited by 7 · see citing works

Newsgroup Classification Using CNN and GloVe Embeddings

Gupta, V., Kumar, A. A., & Bhardwaj, A. (2018) · International Journal of Applied Research on Information Technology and Computing · doi:10.5958/0975-8089.2018.00013.1 · Cited by 2 · see citing works

Machine-Generated Text: A Comprehensive Survey of Threat Models and Detection Methods

Crothers, E. N., Japkowicz, N., & Viktor, H. L. (2023) · IEEE Access · doi:10.1109/access.2023.3294090 · Cited by 196 · see citing works

Contrasting Linguistic Patterns in Human and LLM-Generated News Text

Muñoz-Ortiz, A., Gómez-Rodríguez, C., & Vilares, D. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10903-2 · Cited by 94 · see citing works

TURINGBENCH: A Benchmark Environment for Turing Test in the Age of Neural Text Generation

Uchendu, A., Ma, Z., Le, T., Zhang, R., & Lee, D. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.172 · Cited by 82 · see citing works

ConDA: Contrastive Domain Adaptation for AI-generated Text Detection

Bhattacharjee, A., Kumarage, T., Moraffah, R., & Liu, H. (2023) · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/2023.ijcnlp-main.40 · Cited by 43 · see citing works

Cross-domain detection of GPT-2-generated technical text

Rodriguez, J., Hay, T., Gros, D., Shamsi, Z., & Srinivasan, R. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.88 · Cited by 40 · see citing works

Authorship Attribution in the Era of LLMs: Problems, Methodologies, and Challenges

Huang, B., Chen, C., & Shu, K. (2025) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3715073.3715076 · Cited by 42 · see citing works

Attribution and Obfuscation of Neural Text Authorship: A Data Mining Perspective

Uchendu, A., Le, T., & Lee, D. (2023) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3606274.3606276 · Cited by 34 · see citing works

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) · doi:10.18653/v1/2024.acl-long.218 · Cited by 25 · see citing works

The “Code” of Ethics: A Holistic Audit of AI Code Generators

Ma, W., Song, Y., Xue, M., Wen, S., & Xiang, Y. (2024) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2024.3367737 · Cited by 21 · see citing works

AI-generated or AI touch-up? Identifying AI contribution in text data

Hashemi, A., Shi, W., & Corriveau, J. (2024) · International Journal of Data Science and Analytics · doi:10.1007/s41060-024-00693-9 · Cited by 11 · see citing works

Avatar Fingerprinting for Authorized Use of Synthetic Talking-Head Videos

Prashnani, E., Nagano, K., Mello, S. D., Luebke, D. P., & Gallo, O. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72633-0_12 · Cited by 10 · see citing works

CoAT: Corpus of artificial texts

Shamardina, T., Saidov, M., Fenogenova, A., Tumanov, A., Zemlyakova, A., Lebedeva, A., Gryaznova, E., Shavrina, T., Mikhailov, V., & Artemova, E. (2024) · Natural language processing. · doi:10.1017/nlp.2024.38 · Cited by 8 · see citing works

Fingerprinting Fine-tuned Language Models in the Wild

Diwan, N., Chakraborty, T., & Shafiq, Z. (2021) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2021.findings-acl.409 · Cited by 5 · see citing works

Private Synthetic Text Generation with Diffusion Models

Ochs, S., & Habernal, I. (2025) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/2025.naacl-long.532 · Cited by 1 · see citing works

Evaluating Language Models as Synthetic Data Generators

Kim, S., Suk, J., Yue, X., Viswanathan, V., Lee, S., Wang, Y., Gashteovski, K., Lawrence, C., Welleck, S., & Neubig, G. (2025) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2025.acl-long.320 · Cited by 5 · see citing works

Synthetic Text Generation with Differential Privacy: A Simple and Practical Recipe

Yue, X., Inan, H. A., Li, X., Kumar, G., McAnallen, J., Shajari, H., Sun, H., Levitan, D., & Sim, R. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.74 · Cited by 52 · see citing works

Editing synthetic text from generative artificial intelligence: two exploratory case studies

Farrell, M. (2024) · doi:10.13140/rg.2.2.16045.81128

SynthTextEval: Synthetic Text Data Generation and Evaluation for High-Stakes Domains

Ramesh, K., Smolyak, D., Zhao, Z., Gandhi, N., Agarwal, R., Bjarnadóttir, M. V., & Field, A. (2025) · Conference on Empirical Methods in Natural Language Processing: System Demonstrations (EMNLP) · doi:10.18653/v1/2025.emnlp-demos.35 · Cited by 1 · see citing works