The Imitation Game revisited: A comprehensive survey on recent advances in AI-generated text detection

Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer

Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019) · arXiv · doi:10.48550/arxiv.1910.10683 · Cited by 10,929 · 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 5,058 · see citing works

A kernel two-sample test

(2012) · MPG.PuRe (Max Planck Society) · doi:10.5555/2188385.2188410 · Cited by 3,603 · see citing works

Training Region-Based Object Detectors with Online Hard Example Mining

Shrivastava, A., Gupta, A., & Girshick, R. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.89 · Cited by 2,686 · see citing works

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

(2019) · doi:10.18653/v1/p19-1 · Cited by 2,426 · see citing works

Hierarchical Neural Story Generation

Fan, A., Lewis, M., & Dauphin, Y. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1082 · Cited by 1,266 · see citing works

Hierarchical Neural Story Generation

Fan, A., Lewis, M., & Dauphin, Y. (2018) · arXiv · doi:10.48550/arxiv.1805.04833 · Cited by 1,242 · see citing works

Don’t Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization

Narayan, S., Cohen, S. B., & Lapata, M. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1206 · Cited by 1,207 · see citing works

HotFlip: White-Box Adversarial Examples for Text Classification

Ebrahimi, J., Rao, A., Lowd, D., & Dou, D. (2018) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p18-2006 · Cited by 948 · see citing works

Findings of the Association for Computational Linguistics: EMNLP 2021

(2021) · doi:10.18653/v1/2021.findings-emnlp · Cited by 901 · see citing works

Semi-Supervised Self-Training of Object Detection Models

Rosenberg, C., Hebert, M., & Schneiderman, H. (2005) · 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1 · doi:10.1109/acvmot.2005.107 · Cited by 796 · see citing works

Black-Box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers

Gao, J., Lanchantin, J., Soffa, M. L., & Qi, Y. (2018) · 2018 IEEE Security and Privacy Workshops (SPW) · doi:10.1109/spw.2018.00016 · Cited by 693 · see citing works

Defending Against Neural Fake News

Zellers, R., Holtzman, A., Rashkin, H., Bisk, Y., Farhadi, A., Roesner, F., & Choi, Y. (2019) · arXiv · doi:10.48550/arxiv.1905.12616 · Cited by 569 · see citing works

A Review of Generalized Zero-Shot Learning Methods

Pourpanah, F., Abdar, M., Luo, Y., Zhou, X., Wang, R., Lim, C. P., Wang, X., & Wu, Q. M. J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2022.3191696 · Cited by 493 · see citing works

Supervised Contrastive Learning for Pre-trained Language Model Fine-tuningPreprint

Gunel, B., Du, J., Conneau, A., & Stoyanov, V. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.01403 · Cited by 403 · see citing works

Machine Learning Applications based on SVM Classification A Review

Abdullah, D. M., & Abdulazeez, A. M. (2021) · Qubahan Academic Journal · doi:10.48161/qaj.v1n2a50 · Cited by 377 · see citing works

Findings of the Association for Computational Linguistics: EMNLP 2023

(2023) · doi:10.18653/v1/2023.findings-emnlp · Cited by 350 · see citing works

How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection

Guo, B., Zhang, X., Wang, Z., Jiang, M., Nie, J., Ding, Y., Yue, J., & Wu, Y. (2023) · arXiv · doi:10.48550/arxiv.2301.07597 · Cited by 318 · see citing works

2015 IEEE Security and Privacy Workshops

(2015) · doi:10.1109/spw33959.2015 · Cited by 292 · see citing works

A comparative study of automated legal text classification using random forests and deep learning

Chen, H., Wu, L., Chen, J., Lu, W. P., & Ding, J. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102798 · Cited by 248 · see citing works

Detection of GPT-4 generated text in higher education: Combining academic judgement and software to identify generative AI tool misuse

Perkins, M., Roe, J., Postma, D., McGaughran, J., & Hickerson, D. (2024) · Journal of Academic Ethics · doi:10.1007/s10805-023-09492-6 · Cited by 234 · see citing works

SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation

Hou, A., Zhang, J. (., He, T., Wang, Y., Chuang, Y.-S., Wang, H., Shen, L., Durme, B. V., Khashabi, D., & Tsvetkov, Y. (2024) · North American Chapter of the Association for Computational Linguistics · doi:10.18653/v1/2024.naacl-long.226 · Cited by 200 · see citing works

A Watermark for Large Language Models

Kirchenbauer, J., Geiping, J., Wen, Y., Katz, J., Miers, I., & Goldstein, T. (2023) · arXiv · doi:10.48550/arxiv.2301.10226 · Cited by 194 · see citing works

Can AI-Generated Text be Reliably Detected?

Sadasivan, V. S., Kumar, A., Balasubramanian, S., Wang, W., & Feizi, S. (2023) · arXiv · doi:10.48550/arxiv.2303.11156 · Cited by 161 · see citing works

The hiding virtues of ambiguity

Topkara, U., Topkara, M., & Atallah, M. J. (2006) · ACM workshop on Multimedia and security · doi:10.1145/1161366.1161397 · Cited by 152 · see citing works

New explainability method for BERT-based model in fake news detection

Szczepański, M., Pawlicki, M., Kozik, R., & Choraś, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-03100-6 · Cited by 152 · see citing works

Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defense

Krishna, K., Song, Y., Karpinska, M., Wieting, J., & Iyyer, M. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-1195 · Cited by 150 · see citing works

Text Analytics: the convergence of Big Data and Artificial Intelligence

Sandoval, A. M., & Redondo, T. (2016) · International Journal of Interactive Multimedia and Artificial Intelligence · doi:10.9781/ijimai.2016.369 · Cited by 147 · see citing works

The Science of Detecting LLM-Generated Text

Tang, R., Chuang, Y.-N., & Hu, X. (2024) · Communications of the ACM · doi:10.1145/3624725 · Cited by 125 · see citing works

BOOKSUM: A Collection of Datasets for Long-form Narrative Summarization

Kryściński, W., Rajani, N. F., Agarwal, D., Xiong, C., & Radev, D. (2022) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2022.findings-emnlp.488 · Cited by 122 · see citing works

Text normalization and semantic indexing to enhance Instant Messaging and SMS spam filtering

Almeida, T. A., Silva, T. P., Santos, I., & Hidalgo, J. M. G. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.05.001 · Cited by 105 · see citing works

DetectLLM: Leveraging Log Rank Information for Zero-Shot Detection of Machine-Generated Text

Su, J., Zhuo, T. Y., Wang, D., & Nakov, P. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.827 · Cited by 105 · see citing works

MGTBench: Benchmarking Machine-Generated Text Detection

He, X., Shen, X., Chen, Z., Michael Backes, A., & Zhang, Y. (2024) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3658644.3670344 · Cited by 105 · see citing works

2010 IEEE International Workshop on Information Forensics and Security

(2010) · doi:10.1109/wifs17470.2010 · Cited by 103 · 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

A Review of Digital Watermarking Techniques for Text Documents

Jalil, Z., & Mirza, A. M. (2009) · International Conference on Information and Multimedia Technology · doi:10.1109/icimt.2009.11 · Cited by 81 · see citing works

Automated Extraction of Semantic Legal Metadata using Natural Language Processing

Sleimi, A., Sannier, N., Sabetzadeh, M., Briand, L., & Dann, J. (2018) · IEEE International Requirements Engineering Conference (RE) · doi:10.1109/re.2018.00022 · Cited by 75 · see citing works

Linguistic Markers of Inherently False AI Communication and Intentionally False Human Communication: Evidence From Hotel Reviews

Markowitz, D. M., Hancock, J. T., & Bailenson, J. N. (2023) · Journal of Language and Social Psychology · doi:10.1177/0261927x231200201 · Cited by 71 · see citing works

Evading Watermark based Detection of AI-Generated Content

Jiang, Z., Zhang, J., & Gong, N. Z. (2023) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3576915.3623189 · Cited by 67 · see citing works

CHEAT: A Large-Scale Dataset for Detecting CHatGPT-writtEn AbsTracts

Yu, P., Chen, J., Feng, X., & Xia, Z. (2025) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2025.3536929 · Cited by 65 · see citing works

Few-Shot Detection of Machine-Generated Text using Style RepresentationsPreprint

Soto, R. A. R., Koch, K., Khan, A., Chen, B., Bishop, M., & Andrews, N. (2024) · International Conference on Learning Representations · doi:10.48550/arxiv.2401.06712 · Cited by 64 · see citing works

Natural language watermarking

Topkara, M., Taskiran, C. M., & Delp III, E. J. (2005) · SPIE Proceedings · doi:10.1117/12.593790 · Cited by 63 · see citing works

RADAR: Robust AI-Text Detection via Adversarial Learning

Hu, X., Chen, P.-Y., & Ho, T.-Y. (2023) · Advances in Neural Information Processing Systems 36 · doi:10.52202/075280-0662 · Cited by 62 · see citing works

On the Possibilities of AI-Generated Text Detection

Chakraborty, S., Bedi, A. S., Zhu, S., An, B., Manocha, D., & Huang, F. (2023) · arXiv · doi:10.48550/arxiv.2304.04736 · Cited by 59 · see citing works

Encoded Feature Enhancement in Watermarking Network for Distortion in Real Scenes

Fang, H., Jia, Z., Zhou, H., Ma, Z., & Zhang, W. (2022) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2022.3149641 · Cited by 59 · see citing works

Smaller Language Models are Better Black-box Machine-Generated Text DetectorsPreprint

Mireshghallah, F., Mattern, J., Gao, S.-C., Shokri, R., & Berg-Kirkpatrick, T. (2023) · arXiv.org · doi:10.48550/arxiv.2305.09859 · Cited by 57 · see citing works

Tracing Text Provenance via Context-Aware Lexical Substitution

Yang, X., Zhang, J., Chen, K., Zhang, W., Ma, Z., Wang, F., & Yu, N. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i10.21415 · Cited by 57 · see citing works

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) · doi:10.18653/v1/2024.naacl-long.95 · Cited by 56 · see citing works

SeqXGPT: Sentence-Level AI-Generated Text Detection

Wang, P., Li, L., Ren, K., Jiang, B., Zhang, D., & Qiu, X. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.73 · Cited by 53 · see citing works

Ontology based sentiment analysis for fake review detection

Vidanagama, D. U., Silva, T., & Karunananda, A. S. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117869 · Cited by 51 · see citing works

AI-generated text detection and classification based on BERT deep learning algorithm

Wang, H., Li, J., & Li, Z. (2024) · Theoretical and Natural Science · doi:10.54254/2753-8818/39/20240625 · Cited by 50 · see citing works

Intrinsic Dimension Estimation for Robust Detection of AI-Generated Texts

Tulchinskii, E., Kuznetsov, K., Kushnareva, L., Cherniavskii, D., Nikolenko, S., Burnaev, E. V., Barannikov, S., & Piontkovskaya, I. (2023) · neural information processing systems · doi:10.52202/075280-1706 · Cited by 49 · see citing works

New Approaches for Quantum Copy-Protection

Aaronson, S., Liu, J., Liu, Q., Zhandry, M., & Zhang, R. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84242-0_19 · Cited by 48 · see citing works

LLMDet: A Third Party Large Language Models Generated Text Detection Tool

Wu, K., Pang, L., Shen, H., Cheng, X., & Chua, T. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.139 · Cited by 45 · 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

Robust Multi-bit Natural Language Watermarking through Invariant Features

Yoo, K., Ahn, W., Jang, J., & Kwak, N. (2023) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2023.acl-long.117 · Cited by 43 · see citing works

Provably Robust Multi-bit Watermarking for AI-generated Text via Error Correction CodePreprint

Qu, W., Yin, D., He, Z., Zou, W., Tao, T., Jia, J.-Y., & Zhang, J.-H. (2024) · arXiv.org · doi:10.48550/arxiv.2401.16820 · Cited by 41 · see citing works

Three Bricks to Consolidate Watermarks for Large Language Models

Fernandez, P., Chaffin, A., Tit, K., Chappelier, V., & Furon, T. (2023) · IEEE International Workshop on Information Forensics and Security · doi:10.1109/wifs58808.2023.10374576 · Cited by 39 · see citing works

Multitude: Large-scale multilingual machine-generated text detection benchmark

Macko, D., Moro, R., Uchendu, A., Lucas, J., Yamashita, M., Pikuliak, M., Srba, I., Le, T., Lee, D., Simko, J., et al. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.616 · Cited by 38 · see citing works

Machine-made media: Monitoring the mobilization of machine-generated articles on misinformation and mainstream news websites

Hanley, H. W. A., & Durumeric, Z. (2024) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v18i1.31333 · Cited by 36 · see citing works

ChatGPT paraphrased product reviews can confuse consumers and undermine their trust in genuine reviews. Can you tell the difference?

Xylogiannopoulos, K. F., Xanthopoulos, P., Karampelas, P., & Bakamitsos, G. A. (2024) · Information Processing & Management · doi:10.1016/j.ipm.2024.103842 · Cited by 35 · see citing works

Adaptive Ensembles of Fine-Tuned Transformers for LLM-Generated Text Detection

Lai, Z., Zhang, X., & Chen, S. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651296 · Cited by 35 · see citing works

Large Language Model (LLM) AI text generation detection based on transformer deep learning algorithm

Mo, Y., Qin, H., Dong, Y., Zhu, Z., & Li, Z. (2024) · arXiv · doi:10.48550/arxiv.2405.06652 · Cited by 31 · see citing works

Provable Robust Watermarking for AI-Generated Text

Zhao, X., Ananth, P., Li, L., & Wang, Y.-X. (2023) · arXiv · doi:10.48550/arxiv.2306.17439 · Cited by 30 · see citing works

DiPmark: A Stealthy, Efficient and Resilient Watermark for Large Language ModelsPreprint

Wu, Y.-H., Hu, Z.-M., Zhang, H., & Huang, H. (2023) · arXiv.org · doi:10.48550/arxiv.2310.07710 · Cited by 30 · see citing works

Fast-DetectGPT: Efficient Zero-Shot Detection of Machine-Generated Text via Conditional Probability Curvature

Bao, G., Zhao, Y., Teng, Z., Yang, L., & Zhang, Y. (2023) · arXiv · doi:10.48550/arxiv.2310.05130 · Cited by 28 · see citing works

Safeguarding Authenticity in Text with BERT-Powered Detection of AI-Generated Content

Chakraborty, U., Gheewala, J., Degadwala, S., Vyas, D., & Soni, M. (2024) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict60155.2024.10544590 · Cited by 25 · 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

Language Models Trained on Media Diets Can Predict Public Opinion

Chu, E., Andreas, J., Ansolabehere, S., & Roy, D. (2023) · arXiv · doi:10.48550/arxiv.2303.16779 · Cited by 24 · see citing works

DNA-GPT: Divergent N-Gram Analysis for Training-Free Detection of GPT-Generated Text

Yang, X., Cheng, W., Wu, Y., Petzold, L., Wang, W. Y., & Chen, H. (2023) · arXiv · doi:10.48550/arxiv.2305.17359 · Cited by 23 · see citing works

GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content

Chen, Y., Kang, H., Zhai, V., Li, L., Singh, R., & Raj, B. (2023) · arXiv · doi:10.48550/arxiv.2305.07969 · Cited by 21 · see citing works

Permute-and-Flip: An optimally robust and watermarkable decoder for LLMsPreprint

Zhao, X., Li, L., & Wang, Y.-X. (2024) · arXiv.org · doi:10.48550/arxiv.2402.05864 · Cited by 21 · see citing works

M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection

Wang, Y., Mansurov, J., Ivanov, P., Su, J., Shelmanov, A., Tsvigun, A., Whitehouse, C., Afzal, O. M., Mahmoud, T., Sasaki, T., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.14902 · Cited by 19 · see citing works

Detection Vs. Anti-detection: Is Text Generated by AI Detectable?

Zhang, Y., Ma, Y., Liu, J., Liu, X., Wang, X., & Lu, W. P. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-57850-2_16 · Cited by 19 · see citing works

Multiscale Positive-Unlabeled Detection of AI-Generated TextsPreprint

Tian, Y., Chen, H., Wang, X., Bai, Z., Zhang, Q., Li, R., Xu, C., & Wang, Y. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.18149 · Cited by 17 · see citing works

Are AI-Generated Text Detectors Robust to Adversarial Perturbations?

Huang, G., Zhang, Y., Li, Z., You, Y., Wang, M., & Yang, Z. (2024) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/2024.acl-long.327 · Cited by 16 · see citing works

Watermarking Text Generated by Black-Box Language ModelsPreprint

Yang, X. D., Chen, K., Zhang, W., Liu, C., Qi, Y., Zhang, J., Fang, H., & Yu, N. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.08883 · Cited by 15 · see citing works

Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack

Zhou, Y., He, B., & Sun, L. (2024) · Language Resources and Evaluation Conference (LREC) · doi:10.63317/5ocyse7sux8y · Cited by 14 · see citing works

Raidar: geneRative AI Detection viA Rewriting

Mao, C., Vondrick, C., Wang, H., & Yang, J. (2024) · arXiv · doi:10.48550/arxiv.2401.12970 · Cited by 13 · see citing works

k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text

Hou, A., Zhang, J., Wang, Y., Khashabi, D., & He, T. (2024) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2024.findings-acl.98 · Cited by 13 · see citing works

REMARK-LLM: A Robust and Efficient Watermarking Framework for Generative Large Language ModelsPreprint

Zhang, R., Hussain, S. S., Neekhara, P., & Koushanfar, F. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2310.12362 · Cited by 10 · see citing works

Watermark-based Attribution of AI-Generated ContentPreprint

Jiang, Z., Guo, M., Hu, Y., Wang, Y., & Gong, N. Z. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2404.04254 · Cited by 9 · see citing works

CoCo: Coherence-Enhanced Machine-Generated Text Detection Under Data Limitation With Contrastive LearningPreprint

Liu, X., Zhang, Z., Wang, Y., Pu, H., Lan, Y., & Shen, C. (2022) · arXiv (Cornell University) · doi:10.48550/arxiv.2212.10341 · Cited by 8 · see citing works

HC3 Plus: A Semantic-Invariant Human ChatGPT Comparison CorpusPreprint

Su, Z., Wu, X. C., Zhou, W., Ma, G., & Hu, S. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2309.02731 · Cited by 8 · see citing works

LLM-Detector: Improving AI-Generated Chinese Text Detection with Open-Source LLM Instruction Tuning

Wang, R., Chen, H., Zhou, R., Ma, H., Duan, Y., Kang, Y., Yang, S., Fan, B., & Tan, T. (2024) · arXiv · doi:10.48550/arxiv.2402.01158 · Cited by 5 · see citing works

Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean DiscrepancyPreprint

Zhang, S., Song, Y., Yang, J., Li, Y., Han, B., & Tan, M. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.16041 · Cited by 2 · see citing works

UCL-AST: Active Self-Training with Uncertainty-Aware Clouded Logits for Few-Shot Text Classification

Xu, Y., Hu, J., Gao, Z., & Chen, J. (2022) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai56018.2022.00210 · Cited by 1 · see citing works

Does DetectGPT Fully Utilize Perturbation? Bridging Selective Perturbation to Fine-tuned Contrastive Learning Detector would be BetterPreprint

Liu, S., Liu, X., Wang, Y., Cheng, Z., Li, C., Zhang, Z., Lan, Y., & Shen, C. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.00263 · Cited by 1 · see citing works

Multi-Bit Distortion-Free Watermarking for Large Language ModelsPreprint

Boroujeny, M. K., Jiang, Y., Zeng, K., & Mark, B. L. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2402.16578 · Cited by 1 · see citing works

OPT: Open Pre-trained Transformer Language Models

Zhang, S., Roller, S., Goyal, N., Artetxe, M., Chen, M., Chen, S., Dewan, C., Diab, M., Li, X., Lin, X. V., et al. (2022) · arXiv · doi:10.48550/arxiv.2205.01068

From fake to AI-generated: Leveraging information manipulation theory and explainable AI for robust detection of manipulated reviews

Chernyaeva, O., Lee, O.-K. D., & Hong, T. (2025) · Decision Support Systems · doi:10.1016/j.dss.2025.114522 · Cited by 18 · see citing works

Span-level detection of AI-generated scientific text via contrastive learning and structural calibration

Yin, Z., & Wang, S. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.115123 · Cited by 10 · see citing works

Enhancing the Robustness of AI-Generated Text Detectors: A Survey

Liu, X., Li, Y., & Li, K. (2025) · Mathematics · doi:10.3390/math13132145 · Cited by 10 · see citing works

A Novel Approach for Distinguishing Human and AI-Generated Texts

Abdulhamed, A. A., Ranjan, P., & Xiong, S. (2026) · ACM Transactions on Asian and Low-Resource Language Information Processing · doi:10.1145/3793195 · Cited by 2 · see citing works

Machine-Generated Arabic Fingerprint: Stylometric Analysis and Detection of Large Language Models TextPreprint

Al-shaibani, M. S., & Ahmed, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5350854 · Cited by 8 · see citing works

Arabic machine-generated text detection: Stylometric analysis and cross-model evaluation

Al-shaibani, M. S., & Ahmed, M. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.130644 · Cited by 2 · see citing works

MultiAIGCD: a comprehensive dataset for AI generated code detection covering multiple languages, models, prompts, and scenarios

Demirok, B., Kutlu, M., & Mergen, S. (2026) · Neural Computing and Applications · doi:10.1007/s00521-026-12336-0 · Cited by 1 · see citing works

Artificial Intelligence in the Higher Education System: An Analysis of Institutional Response Scenarios

Chekhovich, Y. V. (2026) · Историческая информатика · doi:10.7256/2585-7797.2026.2.80266 · Cited by 1 · see citing works

Lightweight Llama Models with Experts’ Curated RAG for Electrical Engineering Education: An Exploratory Comparison

Rocha, A., de Oliveira, P. C., Ferreira, J. P., Alves, M., & Sousa, A. J. (2026) · Applied Sciences · doi:10.3390/app16157745 · Cited by 1 · see citing works

Watermarks Beyond English: Towards a Multilingual Evaluation of Watermarks in Large Language ModelsPreprint

Zhang, S., Chen, Y., Han, J., Cheng, B., & Jiang, H. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6595775

Comparative Analysis of Machine Learning Approaches for Automated Detection of AI-Generated Text in Academic Content

Sinha, N., Saravindu, S., Maitra, T., & Giri, D. (2026) · International Conference on Emerging Systems and Intelligent Computing (ESIC) · doi:10.1109/esic68176.2026.11495939

ATM-AM: An explainable AI framework for document classification and sentiment analysis in high-stakes applications

Pittala, R. B., Kiran, M. A., Sharma, N., Thaile, M., Singamaneni, K. K., & Byrapuneni, L. P. (2026) · Expert Systems with Applications · doi:10.1016/j.eswa.2026.133048

Process-Based Authorship Assessment Across Human and LLM-Generated Writing: The Content Restoration Authorship Familiarity Test (CRAFT)Preprint

Rose, R. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6879243

Does the Thesis Still Make Sense? A Comparative Analysis of Scientific Essays Generated by Humans and Generative Artificial Intelligence

Turós, M., Soltész-Várhelyi, K., & Szűts, Z. (2026) · Education Sciences · doi:10.3390/educsci16060920 · Cited by 1 · see citing works

Detection of AI-Obfuscated, AI-Refined, and Humanized AI-Generated Text: A Systematic Review

Sharimbayev, B., & Kadyrov, S. (2026) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.20040 · Cited by 1 · see citing works

The adversarial game between detection and evasion: A survey of anti-detection techniques for machine-generated texts.

Meng, D.-Y., & Gonsalves, T. (2026) · Neural Networks · doi:10.1016/j.neunet.2026.109562

AI-Generated Text Detection with Multi-source Domain Adaptation

Li, S., Zhang, W., Zheng, Z., & Zhang, J. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-31236-5_64

A Systematic Review of AI-Generated Text Detection: Approaches, Tools, and Datasets

Alethary, A. A., & Aliwy, A. H. (2026) · Al-Salam Journal for Engineering and Technology · doi:10.55145/ajest.2026.05.01.012

AI-generated text detection: A comprehensive review of methods, datasets, and applications

Kehkashan, T., Riaz, R. A., Al‐Shamayleh, A. S., Akhunzada, A., Ali, N., Hamza, M., & Akbar, F. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100793 · Cited by 21 · see citing works

Enhancing the Robustness of AI-Generated Text Detectors: A Survey

Liu, X., Li, Y., & Li, K. (2025) · Mathematics · doi:10.3390/math13132145 · Cited by 10 · see citing works

Detecting AI-Generated Text: Factors Influencing Detectability with Current Methods

Fraser, K., Dawkins, H., & Kiritchenko, S. (2025) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1.16665 · Cited by 80 · see citing works

Advancements in AI-Generated Content Forensics: A Systematic Literature Review

Xu, Q., Mu, W., Li, J., Sun, T., & Jiang, X. (2025) · ACM Computing Surveys · doi:10.1145/3760526 · Cited by 16 · see citing works