{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T13:05:39Z","timestamp":1784898339666,"version":"3.55.0"},"reference-count":37,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.eswa.2026.131602","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T16:43:52Z","timestamp":1770741832000},"page":"131602","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["DSAD: A dual-stream aligned detection framework for LLM-generated exam answers"],"prefix":"10.1016","volume":"313","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7938-0028","authenticated-orcid":false,"given":"Jialiang","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3184-6805","authenticated-orcid":false,"given":"Yongjun","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0023-7617","authenticated-orcid":false,"given":"Tao","family":"You","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-6003-1382","authenticated-orcid":false,"given":"Linxin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-0236-1075","authenticated-orcid":false,"given":"Pengyu","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.131602_bib0001","series-title":"Proceedings of the 2024 conference on empirical methods in natural language processing: System demonstrations","first-page":"336","article-title":"LLM-DetectAIve: A tool for fine - grained machine - generated text detection","author":"Abassy","year":"2024"},{"key":"10.1016\/j.eswa.2026.131602_bib0002","unstructured":"Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S. et al. (2023). Gpt-4 technical report. arXiv preprint arXiv: 2303.0874."},{"key":"10.1016\/j.eswa.2026.131602_bib0003","unstructured":"Anthropic (2024). The claude 3 model family: Opus, sonnet, haiku. https:\/\/www.anthropic.com\/news\/claude-3-family."},{"key":"10.1016\/j.eswa.2026.131602_bib0004","series-title":"International conference on representation learning (ICLR)","first-page":"24814","article-title":"Fast-detectGPT: Efficient zero-shot detection of machine-generated text via conditional probability curvature","author":"Bao","year":"2024"},{"key":"10.1016\/j.eswa.2026.131602_bib0005","series-title":"Proceedings of the first conference on machine translation: Volume 2, shared task papers","first-page":"131","article-title":"Findings of the 2016 conference on machine translation","author":"Bojar","year":"2016"},{"key":"10.1016\/j.eswa.2026.131602_bib0006","series-title":"Proceedings of the 2024 conference on empirical methods in natural language processing (EMNLP)","first-page":"8969","article-title":"Postmark: A robust blackbox watermark for large language models","author":"Chang","year":"2024"},{"key":"10.1016\/j.eswa.2026.131602_bib0007","unstructured":"Chen, Y., Kang, H., Zhai, V., Li, L., Singh, R., & Raj, B. (2023). Gpt-sentinel: Distinguishing human and chatgpt generated content. arXiv preprint arXiv: 2305.07969."},{"key":"10.1016\/j.eswa.2026.131602_bib0008","unstructured":"Cobbe, K., Kosaraju, V., Bavarian, M., Chen, M., Jun, H., Kaiser, L., Plappert, M., Tworek, J., Hilton, J., Nakano, R., Hesse, C., & Schulman, J. (2021). Training verifiers to solve math word problems. arXiv preprint arXiv: 2110.14168."},{"issue":"1","key":"10.1016\/j.eswa.2026.131602_bib0009","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1093\/jla\/laae003","article-title":"Large legal fictions: Profiling legal hallucinations in large language models","volume":"16","author":"Dahl","year":"2024","journal-title":"Journal of Legal Analysis"},{"key":"10.1016\/j.eswa.2026.131602_bib0010","unstructured":"Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Yang, A., Fan, A. et al. (2024). The llama 3 herd of models. arXiv preprint arXiv: 2407.21783."},{"key":"10.1016\/j.eswa.2026.131602_bib0011","series-title":"Proceedings of the 57th annual meeting of the association for computational linguistics (ACL)","first-page":"3558","article-title":"ELI5: Long form question answering","author":"Fan","year":"2019"},{"key":"10.1016\/j.eswa.2026.131602_bib0012","series-title":"Proceedings of the 56th annual meeting of the association for computational linguistics (volume 1: Long papers)","first-page":"889","article-title":"Hierarchical neural story generation","author":"Fan","year":"2018"},{"key":"10.1016\/j.eswa.2026.131602_bib0013","doi-asserted-by":"crossref","unstructured":"Frikha, A., Razi, M. R. A., Nakka, K. K., Mendes, R., Jiang, X., & Zhou, X. (2025). Privacyscalpel: Enhancing LLM privacy via interpretable feature intervention with sparse autoencoders. arXiv preprint arXiv: 2503.11232.","DOI":"10.18653\/v1\/2025.blackboxnlp-1.13"},{"key":"10.1016\/j.eswa.2026.131602_bib0014","unstructured":"Gemini Team, Anil, R., Borgeaud, S., Alayrac, J.-B., Yu, J., Soricut, R., Schalkwyk, J., Dai, A. M., Hauth, A., Millican, K. et al. (2023). Gemini: A family of highly capable multimodal models. arXiv preprint arXiv: 2312.11805."},{"key":"10.1016\/j.eswa.2026.131602_bib0015","unstructured":"Gemma Team, Riviere, M., Pathak, S., Sessa, P. G., Hardin, C., Bhupatiraju, S., Hussenot, L., Mesnard, T., Shahriari, B., Ram\u00e9, A., Ferret, J., Liu, P. et al. (2024). Gemma 2: Improving open language models at a practical size. arXiv preprint arXiv: 2408.00118."},{"key":"10.1016\/j.eswa.2026.131602_bib0016","unstructured":"Guo, B., Zhang, X., Wang, Z., Jiang, M., Nie, J., Ding, Y., Yue, J., & Wu, Y. (2023). How close is chatGPT to human experts? comparison corpus, evaluation, and detection. arXiv preprint arXiv: 2301.07597."},{"key":"10.1016\/j.eswa.2026.131602_bib0017","series-title":"Advances in neural information processing systems","first-page":"88320","article-title":"Detective: Detecting AI - generated text via multi - level contrastive learning","author":"Guo","year":"2024"},{"key":"10.1016\/j.eswa.2026.131602_bib0018","series-title":"Proceedings of the 37th international conference on neural information processing systems (neurIPS)","first-page":"15077","article-title":"Radar: Robust ai-text detection via adversarial learning","author":"Hu","year":"2023"},{"key":"10.1016\/j.eswa.2026.131602_bib0019","unstructured":"Jiang, A. Q., Sablayrolles, A., Mensch, A., Bamford, C., Chaplot, D. S., de las Casas, D., Bressand, F., Lengyel, G., Lample, G., Saulnier, L., Lavaud, L. R., Lachaux, M.-A., Stock, P., Le Scao, T., Lavril, T., Wang, T., Lacroix, T., & El Sayed, W. (2023). Mistral 7b. arXiv preprint arXiv: 2310.06825."},{"key":"10.1016\/j.eswa.2026.131602_bib0020","series-title":"Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP)","first-page":"2567","article-title":"PubmedQA: A dataset for biomedical research question answering","author":"Jin","year":"2019"},{"key":"10.1016\/j.eswa.2026.131602_bib0021","series-title":"Proceedings of the 40th international conference on machine learning","first-page":"17061","article-title":"A watermark for large language models","volume":"vol. 202","author":"Kirchenbauer","year":"2023"},{"key":"10.1016\/j.eswa.2026.131602_bib0022","series-title":"Proceedings of the 2008 international conference on uncovering plagiarism, authorship and social software misuse - volume 377 (PAN\u201908)","first-page":"27","article-title":"Detecting fake content with relative entropy scoring","author":"Lavergne","year":"2008"},{"key":"10.1016\/j.eswa.2026.131602_bib0023","series-title":"Proceedings of the 40th international conference on machine learning (ICML\u201923)","first-page":"24950","article-title":"DetectGPT: Zero-shot machine-generated text detection using probability curvature","author":"Mitchell","year":"2023"},{"key":"10.1016\/j.eswa.2026.131602_bib0024","series-title":"Proceedings of the 2018 conference on empirical methods in natural language processing","first-page":"1797","article-title":"Don\u2019T give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization","author":"Narayan","year":"2018"},{"issue":"12","key":"10.1016\/j.eswa.2026.131602_bib0025","first-page":"15999","article-title":"Unmasking academic cheating behavior in the artificial intelligence era: Evidence from vietnamese undergraduates","volume":"29","author":"Nguyen","year":"2024","journal-title":"Education and Information Technologies"},{"key":"10.1016\/j.eswa.2026.131602_bib0026","series-title":"Proceedings of the 2024 conference on empirical methods in natural language processing (EMNLP)","first-page":"22340","article-title":"SimLLM: Detecting sentences generated by large language models using similarity between the generation and its re-generation","author":"Nguyen-Son","year":"2024"},{"key":"10.1016\/j.eswa.2026.131602_bib0027","unstructured":"OpenAI (2022). OpenAI models - GPT3.5. https:\/\/platform.openai.com\/docs\/models\/gpt-3-5."},{"key":"10.1016\/j.eswa.2026.131602_bib0028","series-title":"Proceedings of the 2016 conference on empirical methods in natural language processing","first-page":"2383","article-title":"Squad: 100,000+ questions for machine comprehension of text","author":"Rajpurkar","year":"2016"},{"key":"10.1016\/j.eswa.2026.131602_bib0029","unstructured":"Solaiman, I., Brundage, M., Clark, J., Askell, A., Herbert-Voss, A., Wu, J., Radford, A., Krueger, G., Kim, J. W., Kreps, S., McCain, M., Newhouse, A., Blazakis, J., McGuffie, K., & Wang, J. (2019). Release strategies and the social impacts of language models. arXiv preprint arXiv: 1908.09203."},{"key":"10.1016\/j.eswa.2026.131602_bib0030","series-title":"Findings of the association for computational linguistics: EMNLP 2023","first-page":"12395","article-title":"DetectLLM: Leveraging log rank information for zero-shot detection of machine-generated text","author":"Su","year":"2023"},{"key":"10.1016\/j.eswa.2026.131602_bib0031","series-title":"Proceedings of the 2023 conference on empirical methods in natural language processing (EMNLP)","first-page":"1144","article-title":"SeqXGPT: Sentence-level AI-generated text detection","author":"Wang","year":"2023"},{"key":"10.1016\/j.eswa.2026.131602_bib0032","series-title":"Proceedings of the 41st international conference on machine learning (ICML\u201924)","first-page":"53443","article-title":"A resilient and accessible distribution - preserving watermark for large language models","author":"Wu","year":"2024"},{"issue":"4","key":"10.1016\/j.eswa.2026.131602_bib0033","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3705300","article-title":"Distinguishing LLM-generated from human-written code by contrastive learning","volume":"34","author":"Xu","year":"2025","journal-title":"ACM Transactions on Software Engineering and Methodology"},{"key":"10.1016\/j.eswa.2026.131602_bib0034","unstructured":"Yang, A., Yang, B., Hui, B., Zheng, B., Yu, B., Zhou, C., Li, C., Li, C., Liu, D., Huang, F. et al. (2024a). Qwen2 technical report. arXiv preprint arXiv: 2407.10671."},{"key":"10.1016\/j.eswa.2026.131602_bib0035","series-title":"The twelfth international conference on learning representations (ICLR)","article-title":"DNA-GPT: Divergent n-gram analysis for training-free detection of GPT-generated text","author":"Yang","year":"2024"},{"key":"10.1016\/j.eswa.2026.131602_bib0036","unstructured":"Zhang, X., Li, C., Zong, Y., Ying, Z., He, L., & Qiu, X. (2024). Evaluating the performance of large language models on GAOKAO benchmark. arXiv preprint arXiv: 2305.12474."},{"key":"10.1016\/j.eswa.2026.131602_bib0037","series-title":"The twelfth international conference on learning representations (ICLR)","article-title":"Provable robust watermarking for AI-generated text","author":"Zhao","year":"2024"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426005154?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426005154?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,24]],"date-time":"2026-07-24T12:45:53Z","timestamp":1784897153000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426005154"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":37,"alternative-id":["S0957417426005154"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.131602","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"DSAD: A dual-stream aligned detection framework for LLM-generated exam answers","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.131602","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"131602"}}