MAGE: Machine-generated Text Detection in the Wild
Open accessAbstract
Yafu Li, Qintong Li, Leyang Cui, Wei Bi, Zhilin Wang, Longyue Wang, Linyi Yang, Shuming Shi, Yue Zhang. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Existing supervised classifiers frequently overfit to particular domains, perform poorly on out-of-distribution data, and lose capability on unseen datasets from other domains.
“However, these classifiers often overfit to specific domains, leading to poor performance on out-of-distribution data [12,51], and their capabilities significantly degrade when applied to unseen datasets from different domains [32].”
From Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement (2025) · cites this paper as [32]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
The publisher hasn’t deposited this paper’s reference list.
Research works printed but not linked: 10
- Sameer Badaskar, Sachin Agarwal, and Shilpa Arora. 2008. Identifying real or fake articles: Towards bet- ter language modeling. In Proceedings of the Third International Joint Conference on Natural Language Processing: Volume-II.
- Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. 2020. Language models are few-shot learners. In Ad- vances in Neural Information Processing Systems 33: Annual Conference on Neural Information Process- ing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual.
- Diederik P. Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In 3rd Inter- national Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings.
- Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. 2011. Learning word vectors for sentiment analysis. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 142-150, Portland, Oregon, USA. Association for Computational Lin- guistics.
- Preslav Nakov, Sara Rosenthal, Zornitsa Kozareva, Veselin Stoyanov, Alan Ritter, and Theresa Wilson. 2013. SemEval-2013 task 2: Sentiment analysis in Twitter. In Second Joint Conference on Lexical and Computational Semantics (*SEM), Volume 2: Pro- ceedings of the Seventh International Workshop on Semantic Evaluation (SemEval 2013), pages 312- 320, Atlanta, Georgia, USA. Association for Compu- tational Linguistics.
- F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duch- esnay. 2011. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830.
- Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al. 2019. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9.
- Victor Sanh, Albert Webson, Colin Raffel, Stephen H. Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Arun Raja, Manan Dey, M Saiful Bari, Canwen Xu, Urmish Thakker, Shanya Sharma Sharma, Eliza Szczechla, Taewoon Kim, Gunjan Chhablani, Nihal V. Nayak, Debajyoti Datta, Jonathan Chang, Mike Tian-Jian Jiang, Han Wang, Matteo Manica, Sheng Shen, Zheng Xin Yong, Harshit Pandey, Rachel Bawden, Thomas Wang, Tr- ishala Neeraj, Jos Rozen, Abheesht Sharma, An- drea Santilli, Thibault Févry, Jason Alan Fries, Ryan Teehan, Teven Le Scao, Stella Biderman, Leo Gao, Thomas Wolf, and Alexander M. Rush. 2022. Multi- task prompted training enables zero-shot task gener- alization. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net.
- Rowan Zellers, Ari Holtzman, Hannah Rashkin, Yonatan Bisk, Ali Farhadi, Franziska Roesner, and Yejin Choi. 2019b. Defending against neural fake news. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Informa- tion Processing Systems 2019, NeurIPS 2019, De- cember 8-14, 2019, Vancouver, BC, Canada, pages 9051-9062.
- Xiang Zhang, Junbo Jake Zhao, and Yann LeCun. 2015. Character-level convolutional networks for text clas- sification. In Advances in Neural Information Pro- cessing Systems 28: Annual Conference on Neural In- formation Processing Systems 2015, December 7-12, 2015, Montreal, Quebec, Canada, pages 649-657.
Cited by
Sorted by citations
Quantifying large language model usage in scientific papers
Liang, W., Zhang, Y., Wu, Z., Lepp, H., Ji, W., Zhao, X., Cao, H., Liu, S., He, S., Huang, Z., et al. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02273-8 · Cited by 80 · see citing works
Self-citationDeepfake: definitions, performance metrics and standards, datasets, and a meta-review
Altuncu, E., Franqueira, V. N. L., & Li, S. (2024) · Frontiers in Big Data · doi:10.3389/fdata.2024.1400024 · Cited by 65 · 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
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
“LLM-generated text detectors often struggle to generalize to unseen domains encountered during training (Li et al., 2023a, Pu et al., 2023b, Rodriguez et al., 2022) and tend to perform better on LLMs they were specifically trained on (Chakraborty et al., 2023b, Li et al., 2023a, Pu et al., 2023b).”
Stylometry recognizes human and LLM-generated texts in short samples
Przystalski, K., Argasiński, J. K., Grabska-Gradzińska, I., & Ochab, J. K. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129001 · Cited by 24 · see citing works
Santos, A. M., & António, N. (2025) · Information Technology & Tourism · doi:10.1007/s40558-025-00329-z · Cited by 14 · see citing works
Sun, Y., Liao, Y.-H., & Ma, X.-X. (2026) · Comput. Educ. · doi:10.1016/j.compedu.2026.105616 · Cited by 17 · see citing works
Srba, I., Razuvayevskaya, O., Leite, J. A., Moro, R., Schlicht, I. B., Tonelli, S., García, F. M., Lottmann, S. B., Teyssou, D., Porcellini, V., et al. (2026) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3770077 · Cited by 19 · see citing works
Wang, Q., & Li, H. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9030050 · Cited by 12 · see citing works
Zero-shot detection of LLM-generated text via text reorder
Sun, J., & Lv, Z. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.129829 · 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
Self-citationInvestigation Into the Identification of AI-Generated Short Dialectal Arabic Texts
Alharthi, H. (2025) · IEEE Access · doi:10.1109/access.2025.3568696 · Cited by 8 · see citing works
Artificial Intelligence vs. Human: Decoding Text Authenticity with Transformers
Gîfu, D., & Silviu-Vasile, C. (2025) · Future Internet · doi:10.3390/fi17010038 · Cited by 10 · see citing works
Misinformation and Disinformation in Generative AI—A Survey
Srinivasan, R. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-8183-9_23 · Cited by 5 · see citing works
González, P. M., Huertas‐Tato, J., Martín, A. G., & Camacho, D. (2025) · Information Fusion · doi:10.1016/j.inffus.2025.103465 · Cited by 9 · see citing works
A novel approach to identify deepfake text using social media data
Devi, B. T., & Rajasekaran, R. (2025) · Social Network Analysis and Mining · doi:10.1007/s13278-025-01536-6 · Cited by 5 · see citing works
Privacy dilemmas and opportunities in large language models: a brief review
Li, H., Ye, J., & Wu, J. (2025) · Frontiers of Computer Science · doi:10.1007/s11704-024-40583-8 · Cited by 6 · see citing works
On the Trustworthiness Landscape of State-of-the-art Generative Models: A Survey and Outlook
Fan, M., Wang, C., Chen, C., Liu, Y., & Huang, J. (2025) · International Journal of Computer Vision · doi:10.1007/s11263-025-02375-w · Cited by 17 · see citing works
Stack More LLM’s: Efficient Detection of Machine-Generated Texts via Perplexity Approximation
Gritsai, G., Khabutdinov, I., & Grabovoy, A. (2024) · Doklady Mathematics · doi:10.1134/s1064562424602075 · Cited by 6 · see citing works
LOCAT: Localization-Driven Text Watermarking via Large Language Models
Ding, L., Yang, X., Yang, Y., & Zhang, W. (2025) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2025.3570858 · Cited by 4 · see citing works
GigaCheck: Detecting LLM-generated ContentPreprint
Tolstykh, I., Tsybina, A., Yakubson, S., Gordeev, A., Dokholyan, V., & Kuprashevich, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5381590 · Cited by 5 · see citing works
Cheng, Z., Zhou, L., Jiang, F., Wang, B., & Li, H. (2025) · Proceedings of the ACM on Web Conference 2025 · doi:10.1145/3696410.3714770 · Cited by 3 · see citing works
Beyond N-Grams: Enhancing String Kernels With Transformer-Guided Semantic Insights
Zaki, N., Alderei, R., Alketbi, M., Alkaabi, A., Alneyadi, F., & Zaki, N. (2025) · IEEE Access · doi:10.1109/access.2025.3576076 · Cited by 4 · see citing works
N-Gram Perplexity-Based AI-Generated Text Detection
Dmitrii, P., Mestetsky, L., & Grabovoy, A. (2024) · Ivannikov ISPRAS Open Conference (ISPRAS) · doi:10.1109/ispras64596.2024.10899150 · Cited by 2 · see citing works
Continual Origin Tracing of LLM-Generated Text
Li, H., & Wang, Q. (2025) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3726302.3729935 · Cited by 2 · see citing works
Human or Machine? A Survey on Machine-Generated Text Detection
Ahmad, Z., Torres-Ruiz, M. J., Mahmood, A., Quintero, R., Ameer, I., & Bölücü, N. (2026) · IEEE Access · doi:10.1109/access.2026.3666781 · Cited by 2 · see citing works
Wu, W.-Q., Zhang, C.-Z., Zhao, Y., & Bao, T. (2026) · Scientometrics · doi:10.1007/s11192-026-05645-7 · Cited by 2 · see citing works
AI text detectors and the misclassification of slightly polished Arabic text
Almohaimeed, S., Almohaimeed, S., Jari, M., Alobaid, K. A., & Alotaibi, F. (2026) · Journal Of Big Data · doi:10.1186/s40537-026-01492-8 · Cited by 2 · see citing works
Albert Gatt, A. (2025) · Oxford University Press eBooks · doi:10.1093/acrefore/9780199384655.013.896 · Cited by 2 · see citing works
Detecting AI-Generated Text: A Bi-GRU with Linguistic Features Approach
Hireche, A., Al-Dabet, S., Mediani, M., & Belkacem, A. N. (2025) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon62633.2025.11016416 · Cited by 2 · see citing works
Deepfake Text Detection Using NLP and ML
Akashdeep, Mitra, H., & Gashroo, O. B. (2025) · International Conference on Computing, Intelligence, and Application (CIACON) · doi:10.1109/ciacon65473.2025.11189404 · Cited by 1 · see citing works
Zhou, L., Zhang, R., Dai, X., Hershcovich, D., & Li, H. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114988 · Cited by 3 · see citing works
SHALLOW ANN MODELS TO CLASSIFY UKRAINIAN AI-GENERATED TEXT
Peredrii, O. (2025) · Системи управління навігації та зв’язку Збірник наукових праць · doi:10.26906/sunz.2025.4.108 · Cited by 2 · see citing works
Efficient detection of AI-generated scientific abstracts with a lightweight transformer
Zhang, C., & Zhou, W. (2026) · Scientific Reports · doi:10.1038/s41598-026-35203-3 · Cited by 2 · see citing works
A Survey on AI-Generated Text Detection: Methods, Challenges, and Future Directions
Ismail, A. A., Ahmed, C., & ElKorany, A. (2025) · Future International Conference on Artificial Intelligence and Cybersecurity (FICAC) · doi:10.1109/ficac65757.2025.11341850 · Cited by 1 · see citing works
Öner, İ., & Özbay, E. (2026) · Applied Sciences · doi:10.3390/app16105060 · Cited by 1 · see citing works
On the Gap Between AI-Generated and Human-Written Patent Texts
Xiao, Z., Hu, W. P., Wu, Y., Chen, W., Li, H., & Liu, X. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-2882-7_14
A General Method for Detecting Information Generated by Large Language ModelsPreprint
Mao, M., Wei, D., Fang, X., & Chau, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5309665
Detecting Large Language Model Generated Korean Essays Using Curriculum LearningPreprint
Jung, Y., & Oh, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5380275
Checkpoint Cascading Prompt: A Novel Approach to Text RewritingPreprint
mahmood, A., Ahmad, Z., Mahmood, M., & Farid, H. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5413943
MedAID-ML: A Multilingual Dataset of Biomedical Texts for Detecting AI-Generated Content
Styll, P., Llanos, L. C., Fernández-García, J., & Segura-Bédmar, I. (2025) · Lecture notes in computer science · doi:10.1007/978-3-032-04354-2_5
Exploring Generalized Features For LLM-Generated Text Detection
Wang, J., Liu, B., Miao, C., Wang, Y., Gong, T., Chu, Q., Zou, Q., Zhang, D., & Yu, N. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3729-7_25
Yan, J., Luo, Y., & Zhang, Y. (2025) · IEEE Transactions on Audio Speech and Language Processing · doi:10.1109/taslpro.2025.3626926
Self-citationOverview of the NLPCC 2025 Shared Task 1: LLM-Generated Text Detection
Wu, J., Zhan, R., Wang, Q., Yuan, Y., Chao, L. S., & Wong, D. F. (2025) · Lecture notes in computer science · doi:10.1007/978-981-95-3352-7_21
An empirical comparative study to detect AI generated scientific text using statistical featuresPreprint
Chizari, H., Chizari, M., Malekinezhad, F., Alam, A., & Cook, R. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5869483
Detection of AI-Generated texts with BERT variants
Balara, V., & Machová, K. (2025) · International Conference on Emerging eLearning Technologies and Applications (ICETA) · doi:10.1109/iceta67772.2025.11280096
MoE-OPU: An FPGA Overlay Processor Leveraging Expert Parallelism for MoE-based Large Language Models
Lu, S., Wei, Y., Qian, J., Wu, C., Shi, X., & He, L. (2025) · doi:10.1109/iccad66269.2025.11240807
Liu, X. A., & Jin, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5986968
A Comprehensive Survey of Machine-Generated Text Detection
孙, 一. (2025) · Artificial Intelligence and Robotics Research · doi:10.12677/airr.2026.151005
Vaidya, S., & Saini, J. R. (2025) · International Conference for Advancement in Technology (ICONAT) · doi:10.1109/iconat66879.2025.11362514
Research on Large Language Model Text Source Detection Based on Domain Adaptive Fine-Tuning
Nan, Z., & Wang, Y. (2025) · International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI) · doi:10.1109/cei66465.2025.11398514
A Training-free Method for LLM Text AttributionPreprint
Radvand, T., Abdolmaleki, M., Mostagir, M., & Tewari, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6169527
Min, H., Guo, F., Sun, T., Liu, M., & Oswald, F. L. (2026) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459261419816
MDD-TD: A dataset of large language model text source and content authenticity detection
Li, S., & Ning, M. A. (2026) · China Scientific Data · doi:10.11922/11-6035.csd.2025.0191.zh
“Fasttext-Fused Hybrid Neural Ensemble Framework for Authenticity Verification in Tweets”
Veerabrahmam, B., Gangotri, K., Jayavijaya, S., Jyothi, A., Venkatesh, M., & Siri, D. (2026) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi68868.2026.11465358
Liu, X. R., Wang, S., Li, Y., & Li, K. (2026) · Intelligent Data Analysis · doi:10.1177/1088467x261441866
Self-citationDetectWild: In-The-Wild AI-Generated Text Detection Benchmark
Zhang, L., Guo, X., Zhao, Z., Cai, S., Wang, Q., Xu, W., Lin, X., & Cui, W. (2026) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp55912.2026.11460813
Meem, S. J., Samsuddoha, M., Momo, S. J., Erfan, M., & Ahmed, M. M. (2026) · Applied Soft Computing · doi:10.1016/j.asoc.2026.116261
False Alarm or Real Threat? Trends in GenAI-Mediated Disinformation
Papadopoulou, O., Teyssou, D., Macko, D., & Papadopoulos, S. (2026) · Springer eBooks · doi:10.1007/978-3-032-11782-3_2
Evaluating Perplexity as an Indicator for Sentence-Level AI-Generated Text Detection
Bhagya, M., Chouhan, S. S., Singh, K., & Tripathi, A. K. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-18907-3_2
LiMO: Line-level Machine-generated Code Detection for Diverse Programming Languages
Kim, T., Jang, H., Kim, S., Jin, Z., Kim, S., & Bak, J. (2026) · ACM Symposium on Applied Computing · doi:10.1145/3748522.3779718
PromptFishing: Active Hallucination Inducement to Distinguish LLMs From Humans
Chen, B., Li, G., Wu, J., Li, J., & Fang, H. (2026) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2026.3709099
Cross-model evaluation of phishing detectors against LLM-generated emails
Gutierrez, R., Villegas-Ch, W. E., & Govea, J. (2026) · Frontiers in Big Data · doi:10.3389/fdata.2026.1883452 · Cited by 1 · see citing works
LLM-Oriented Information Retrieval: A Denoising-First Perspective
Dai, L., Sun, L., Cao, F., Rao, Z., Yang, C., Liu, H., & Xiong, H. (2026) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3805712.3808544
Tefke, T., & Staudemeyer, R. C. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-32335-4_11
Villacura-Herrera, C., Zaal, F., & Cox, R. F. A. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.101221
Too Simple or Too Complex? Using Linguistic Signatures for AI-Generated Text Detection
Schäfer, K., & Bassenge, M. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31404-8_33
Yang, Y., Huang, E., Xia, C., Yang, B., Pan, T., Zhang, Z., Dong, D., & Qin, Z. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-31335-5_22
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
Alsaeedi, A., AlMansour, A. A., & Jamal, A. T. (2026) · Applied Sciences · doi:10.3390/app16168287
Combining probabilistic features and semantic features for AI-Generated text detection
Yu, Y., & Gao, W. (2026) · International Journal of Scientific Research and Management (IJSRM) · doi:10.18535/ijsrm/v14i09.ec01
Ramani, A., & Venugopalan, M. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-02106-1
Benchmark-Task Heterogeneity in Misinformation-Related and Human–AI Text Classification
Swierk, G., & Olszowski, R. (2026) · Information · doi:10.3390/info17090866
Tüker, M., Sınecen, M., & Mehmet Taha, E. S. E. R. (2026) · Scientific journal of Mehmet Akif Ersoy University. · doi:10.70030/sjmakeu.2033004
Zhang, Z. (2026) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001426400616
Evaluating Robustness of AI-Generated Text Detectors Under Simulated Iterative Obfuscation
Sharimbayev, B., Kadyrov, S., Akhmetov, A., & Ulasbekov, A. (2026) · 2026 IEEE 1st International Conference on Artificial Intelligence Implementation & Applications (ICAIIA) · doi:10.1109/icaiia70172.2026.11691861
Füzesi, I., Lengyel, P., & Pancsira, J. (2026) · International Review of Applied Sciences and Engineering · doi:10.1556/1848.2026.01535
SAMP: source-aware multi-prototype learning for machine-generated text detection
Xu, Y., Yang, W., Yin, Y., Lv, H., Wang, Z., He, J., Jia, X., & Wang, X. (2026) · Journal of King Saud University - Computer and Information Sciences · doi:10.1007/s44443-026-01331-z
Self-citationImpact of clustering and classification on TweepFake - Twitter Deep Fake Text Dataset
Rath, S., Rautaray, S. S., & Pandey, M. (2026) · International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) · doi:10.1109/assic70037.2026.11717457
Detection of Bangla AI ‐Generated Texts Using a Quantum Kernel Support Vector Machine
Ansary, M. S. (2026) · Applied AI Letters · doi:10.1002/ail2.70051
Nurislom, A. (2026) · Eurasian Journal of Mathematical Theory and Computer Sciences · doi:10.70413/ejmtcs-v06-i10-s92242
- Authors
- Li, Y., Li, Q., Cui, L., Bi, W., Wang, Z., Wang, L., Yang, L., Shi, S., & Zhang, Y.
- Affiliations
- Jilin University (CN)
- Venue
- Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Published
- 2024
- Pages
- 36-53
- ISSN
- 0736-587X (ISSN-L)
- OpenAlex
- W4402667057
- Cited by
- 79
- References
- 0
- Citations by year
- 2026: 37 · 2025: 39 · 2024: 3
- FWCI
- 23.31
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Proceedings of the conference - Association for Computational Linguistics. Meeting (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Authorship Attribution and Profiling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Keywords
- AI-generated text detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
