Distribution-Free Selective Prediction for LLM-RAG Hallucination Detection: Word-Level Conformal Risk Control under Domain Shift
DOI:
https://doi.org/10.61424/ka6yps02Keywords:
Retrieval-augmented generation; hallucination detection; word-level classification; conformal prediction; selective prediction; probability calibration; domain shift; RAGTruth.Abstract
Retrieval-augmented generation (RAG) gives language models access to external evidence, yet a generated answer can still contain unsupported or contradictory words. This study evaluates word-level hallucination detection and uncertainty control on the complete RAGTruth corpus: 17,790 responses derived from 2,965 evidence sources across question answering, summarization, and data-to-text generation. To prevent evidence leakage, source identifiers were partitioned before any response or token was assigned to fit, development, calibration, and test sets. Three computationally light detectors were trained and evaluated: a deterministic evidence-overlap rule, a 131,072-dimensional hashed logistic model, and a compact multilayer perceptron using 55 dense lexical, local-context, and evidence-alignment features. Platt calibration converted detector scores to probabilities, after which pooled split conformal, task-Mondrian conformal, and source-block maximum calibration produced binary prediction sets at nominal coverage levels of 0.95, 0.90, and 0.80. The hashed logistic detector achieved the strongest thresholded token F1 (0.294), compared with 0.270 for the compact MLP and 0.117 for the rule, while the MLP achieved the highest AUROC (0.813) and lowest expected calibration error (0.0099). At 90% nominal coverage, pooled and task-Mondrian prediction sets obtained 0.917 and 0.918 empirical coverage with mean set sizes of 0.945 and 0.949. Source-block maximum calibration covered every test token but always returned both labels, demonstrating the cost of conservative cluster protection. Leave-one-task-out experiments revealed substantial shift sensitivity: empirical coverage ranged from 0.752 on held-out data-to-text generation to 0.970 on held-out summarization. The results establish a reproducible, source-disjoint benchmark for selective word-level RAG auditing and clarify the boundary between finite-sample marginal coverage under exchangeability and empirical robustness under task shift.
References
Angelopoulos, A. N., & Bates, S. (2023). Conformal prediction: A gentle introduction. Foundations and Trends in Machine Learning, 16(4), 494–591. https://doi.org/10.1561/2200000101
Angelopoulos, A. N., Bates, S., Fisch, A., Lei, L., & Schuster, T. (2024). Conformal risk control. In The Twelfth International Conference on Learning Representations. https://proceedings.iclr.cc/paper_files/paper/2024/hash/f3549ef9b5ff520a7e41ff3cc306ab2b-Abstract-Conference.html
Bai, J., Chen, S., Zheng, D., & Kuo, M.-J. (2026). Interpretable attack-chain stage detection from AWS CloudTrail event sequences via linear models and HMM smoothing. Information, Electrical and Electronics Engineering, 6(1), 28–43. https://doi.org/10.33474/infotron.v6i1.24923
Barber, R. F., Candès, E. J., Ramdas, A., & Tibshirani, R. J. (2023). Conformal prediction beyond exchangeability. The Annals of Statistics, 51(2), 816–845. https://doi.org/10.1214/23-AOS2276
Bates, S., Angelopoulos, A. N., Lei, L., Malik, J., & Jordan, M. I. (2021). Distribution-free, risk-controlling prediction sets. Journal of the ACM, 68(6), Article 43, 1–34. https://doi.org/10.1145/3478535
Campos, M., Farinhas, A., Zerva, C., Figueiredo, M. A. T., & Martins, A. F. T. (2024). Conformal prediction for natural language processing: A survey. Transactions of the Association for Computational Linguistics, 12, 1497–1516. https://doi.org/10.1162/tacl_a_00715
Chang, X., Lu, Y., & Zhong, Z. S. (2026). Review-grounded explainable recommendation with faithfulness evaluation on Amazon reviews. Journal of Electrical Engineering and Computer Sciences, 11(1), 9–22. https://doi.org/10.54732/jeecs.v11i1.2
Chen, S., He, S., & Sun, E. (2024). Risk-bounded GPU resource oversubscription via conformal demand envelopes in production AI clusters. Journal of Advanced Computing Systems, 4(5), 119–134. https://doi.org/10.69987/JACS.2024.40509
Chen, Y., & Xu, H. (2026). Trust-calibrated multilingual RAG for humanitarian information platforms: Empirical evaluation on OMoS-QA for migration information access. International Journal of Graphic Design, 4(1), 141–164. https://doi.org/10.51903/ijgd.v4i1.3552
Chen, Y., Zhang, Y., & Sherman, M. (2024). Going concern and bankruptcy prediction under extreme class imbalance: Cost-sensitive learning, resampling, and focal loss with explainable financial-ratio portraits. Journal of Advanced Computing Systems, 4(4), 80–96. https://doi.org/10.69987/JACS.2024.40407
Chen, Y., Zhang, Y., Chau, D., & Sherman, M. (2023). Credit card default risk tiering with probability calibration and uncertainty-driven rejection: A reproducible study on the UCI credit card clients dataset. Journal of Advanced Computing Systems, 3(4), 31–47. https://doi.org/10.69987/JACS.2023.30403
Chen, Y., Zhou, S., & Lin, E. (2025). Accounting-aware evidence retrieval for institutional due diligence of tokenized trade receivable RWA. Journal of Technology Informatics and Engineering, 4(3), 649–663. https://doi.org/10.51903/jtie.v4i3.542
Desai, S., & Durrett, G. (2020). Calibration of pre-trained transformers. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (pp. 295–302). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.21
Dziri, N., Kamalloo, E., Milton, S., Zaiane, O., Yu, M., Ponti, E. M., & Reddy, S. (2022). FaithDial: A faithful benchmark for information-seeking dialogue. Transactions of the Association for Computational Linguistics, 10, 1473–1490. https://doi.org/10.1162/tacl_a_00529
El-Yaniv, R., & Wiener, Y. (2010). On the foundations of noise-free selective classification. Journal of Machine Learning Research, 11(53), 1605–1641. https://jmlr.org/papers/v11/el-yaniv10a.html
Es, S., James, J., Espinosa Anke, L., & Schockaert, S. (2024). RAGAs: Automated evaluation of retrieval augmented generation. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations (pp. 150–158). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.eacl-demo.16
Farinhas, A., Zerva, C., Ulmer, D., & Martins, A. F. T. (2024). Non-exchangeable conformal risk control. In The Twelfth International Conference on Learning Representations. https://proceedings.iclr.cc/paper_files/paper/2024/hash/de04896f011beff76c91e094f72727f4-Abstract-Conference.html
Gao, T., Yen, H., Yu, J., & Chen, D. (2023). Enabling large language models to generate text with citations. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 6465–6488). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.398
Geifman, Y., & El-Yaniv, R. (2019). SelectiveNet: A deep neural network with an integrated reject option. In Proceedings of the 36th International Conference on Machine Learning (pp. 2151–2159). PMLR. https://proceedings.mlr.press/v97/geifman19a.html
Gibbs, I., & Candès, E. J. (2021). Adaptive conformal inference under distribution shift. Advances in Neural Information Processing Systems, 34, 1660–1672. https://proceedings.neurips.cc/paper/2021/hash/0d441de75945e5acbc865406fc9a2559-Abstract.html
Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. In Proceedings of the 34th International Conference on Machine Learning (pp. 1321–1330). PMLR. https://proceedings.mlr.press/v70/guo17a.html
He, S., Chang, X., & Sun, E. (2024). Cross-cloud transfer learning for AI training capacity forecasting under workload and topology distribution shift. Journal of Advanced Computing Systems, 4(1), 100–120. https://doi.org/10.69987/JACS.2024.40108
He, S., Li, C., & Rao, H. (2025). Few-shot cold-start workload forecasting for new AI inference tenants with time-series foundation models. Journal of Technology Informatics and Engineering, 4(1), 306–324. https://doi.org/10.51903/jtie.v4i1.546
He, S., Tu, H., & Liu, I. (2023). Safe PD capacity forecasting with time-series foundation models and calibrated uncertainty for heterogeneous GPU clusters. Journal of Advanced Computing Systems, 3(4), 48–66. https://doi.org/10.69987/JACS.2023.30404
Honovich, O., Aharoni, R., Herzig, J., Taitelbaum, H., Kukliansy, D., Cohen, V., Scialom, T., Szpektor, I., Hassidim, A., & Matias, Y. (2022). TRUE: Re-evaluating factual consistency evaluation. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (pp. 3905–3920). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.naacl-main.287
Jiang, Z., Araki, J., Ding, H., & Neubig, G. (2021). How can we know when language models know? On the calibration of language models for question answering. Transactions of the Association for Computational Linguistics, 9, 962–977. https://doi.org/10.1162/tacl_a_00407
Jin, J. (2025a). Evidence-chain reliable RAG: Hallucination detection, source attribution, and deterministic provenance explanations. Journal of Technology Informatics and Engineering, 4(2), 520–533. https://doi.org/10.51903/jtie.v4i2.535
Jin, J. (2025b). LLM-style evidence cards for scientific search interfaces: A UI/UX design framework for retrieval transparency, ranking trust, and visual evidence hierarchy. International Journal of Graphic Design, 3(2), 397–414. https://doi.org/10.51903/ijgd.v3i2.3698
Jin, J., Huang, T., & Lu, S. (2024a). Cost-sensitive learning, simulated PU learning, and one-class autoencoding for extreme-imbalance credit card fraud detection. Journal of Advanced Computing Systems, 4(6), 64–73. https://doi.org/10.69987/JACS.2024.40605
Jin, J., Huang, T., & Lu, S. (2024b). A model-risk-friendly probability of default workflow: Calibration, distribution-free uncertainty quantification, and SHAP explanations on the UCI credit card default dataset. Journal of Advanced Computing Systems, 4(6), 74–85. https://doi.org/10.69987/JACS.2024.40606
Kamath, A., Jia, R., & Liang, P. (2020). Selective question answering under domain shift. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 5684–5696). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.acl-main.503
Kuo, M.-J., Zheng, D., & Hires, J. (2025). Federated topic-preference learning for knowledge-grounded chat with differential privacy. Journal of Technology Informatics and Engineering, 4(2), 385–401. https://doi.org/10.51903/jtie.v4i2.502
Lewis, P., Perez, E., Piktus, A., Petroni, F., Karpukhin, V., Goyal, N., Küttler, H., Lewis, M., Yih, W.-t., Rocktäschel, T., Riedel, S., & Kiela, D. (2020). Retrieval-augmented generation for knowledge-intensive NLP tasks. Advances in Neural Information Processing Systems, 33, 9459–9474. https://proceedings.neurips.cc/paper/2020/hash/6b493230205f780e1bc26945df7481e5-Abstract.html
Li, C., Bai, J., & Wang, S. (2024). Evidence-chain reliable RAG: Word-level hallucination detection, source attribution, and provenance explanation for LLM applications. Journal of Advanced Computing Systems, 4(2), 76–92. https://doi.org/10.69987/JACS.2024.40207
Li, C., Liu, G., & Zhao, Z. (2026). Cost-aware LLM-style routing for AIOps log analysis: Log parsing, anomaly detection, fault diagnosis, and incident summarization on LogEval task files. Journal of Technology Informatics and Engineering, 5(2), 91–103. https://doi.org/10.51903/jtie.v5i2.538
Li, C., Zhou, B., & Gao, K. (2025). Risk-calibrated patient-facing AI safety cards: A UI/UX benchmark for explainable medical AI response interfaces. International Journal of Graphic Design, 3(2), 381–394. https://doi.org/10.51903/ijgd.v3i2.3709
Li, J., & Zhou, A. (2026). Multi-regulation RAG for AI product counsel: A legal governance framework for cross-border digital commerces. Rule of Law Studies Journal, 2(2), 105–123. https://doi.org/10.64780/rolsj.v2i2.225
Li, J., Cheng, X., Zhao, X., Nie, J.-Y., & Wen, J.-R. (2023). HaluEval: A large-scale hallucination evaluation benchmark for large language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 6449–6464). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.397
Li, Y. (2024). Findable then explainable: Retrieval–summary integration for code intelligence on a lightweight CodeSearchNet subset. Journal of Advanced Computing Systems, 4(7), 65–82. https://doi.org/10.69987/JACS.2024.40706
Li, Y., & Lu, S. (2025). Language-guided feature selection for DDoS and intrusion detection on CICIDS2017. Journal of Technology Informatics and Engineering, 4(1), 284–305. https://doi.org/10.51903/jtie.v4i1.531
Li, Y., Lu, S., & Zhao, L. (2025). LLM-as-design-critic: Aligning AI-generated UI feedback with human graphic design judgment. International Journal of Graphic Design, 3(1), 196–215. https://doi.org/10.51903/ijgd.v3i1.3661
Li, Z., Zhang, K., & Wong, A. (2026). Numerical-reasoning guardrails for a quant research assistant: A compact reproducible benchmark using SEC and FRED data. Journal of Technology Informatics and Engineering, 5(2), 75–90. https://doi.org/10.51903/jtie.v5i2.541
Li, Z., Zhou, S., & Zhou, Z. (2025). Financial risk dashboard design for institutional RWA investors: Visual hierarchy, chart comprehension, and explainability in FinChart-Bench. International Journal of Graphic Design, 3(1), 196–210. https://doi.org/10.51903/ijgd.v3i1.3715
Liu, G., He, S., & Liu, I. (2023). LLM-augmented multi-source root cause attribution for CPU and network faults in microservices. Journal of Advanced Computing Systems, 3(6), 39–57. https://doi.org/10.69987/JACS.2023.30604
Liu, G., He, S., & Wong, H. (2025). LLM-compatible visual brief cards for AI infrastructure capacity dashboards: A UI/UX framework for turning forecast risk into graphic design decisions. International Journal of Graphic Design, 3(1), 196–213. https://doi.org/10.51903/ijgd.v3i1.3723
Liu, G., Li, C., & Zhang, E. (2024). OpsLLM for cloud incident triage: Bilingual RAG-based root cause analysis and alert summarization for AI infrastructure operations. Journal of Advanced Computing Systems, 4(4), 97–111. https://doi.org/10.69987/JACS.2024.40408
Liu, T., Zhang, Y., Brockett, C., Mao, Y., Sui, Z., Chen, W., & Dolan, B. (2022). A token-level reference-free hallucination detection benchmark for free-form text generation. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 6723–6737). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.acl-long.464
Lu, S., & Zhou, D. (2024). TinyLLM-assisted intrusion detection for real-time IoT networks. Journal of Advanced Computing Systems, 4(8), 72–87. https://doi.org/10.69987/JACS.2024.40809
Lu, S., & Zou, T. (2026). Uncertainty-aware medical vision–language classification on a lightweight MedMNIST-compatible biomedical patch benchmark. Journal of Technology Informatics and Engineering, 5(2), 1–19. https://doi.org/10.51903/jtie.v5i2.530
Manakul, P., Liusie, A., & Gales, M. (2023). SelfCheckGPT: Zero-resource black-box hallucination detection for generative large language models. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 9004–9017). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.557
Meng, S., Chen, J., & Zheng, I. (2026). LLM-inspired offline reranking for financial search: Query rewriting, hybrid retrieval, and listwise relevance ranking on FiQA. Journal of Technology Informatics and Engineering, 5(1), 361–378. https://doi.org/10.51903/jtie.v5i1.537
Mi, G., Ye, T., & Wood, D. (2025). A lightweight medical foundation model for cross-modal multi-task pretraining and parameter-efficient few-shot transfer on MedMNIST. Journal of Technology Informatics and Engineering, 4(3), 572–589. https://doi.org/10.51903/jtie.v4i3.492
Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W.-t., Koh, P. W., Iyyer, M., Zettlemoyer, L., & Hajishirzi, H. (2023). FActScore: Fine-grained atomic evaluation of factual precision in long-form text generation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (pp. 12076–12100). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.emnlp-main.741
Nie, J., & Zheng, D. (2024). Noisy-neighbor-aware VM degradation risk modeling with unsupervised residual fusion. Journal of Advanced Computing Systems, 4(4), 112–123. https://doi.org/10.69987/JACS.2024.40409
Nie, J., Liu, G., Li, C., & Zou, T. (2026). Evidence-constrained incident visualization cards for distributed cloud logs: A UI/UX framework for turning Hadoop, OpenStack, and ZooKeeper logs into actionable SRE design interfaces. International Journal of Graphic Design, 4(1), 179–185. https://doi.org/10.51903/ijgd.v4i1.3703
Niu, C., Wu, Y., Zhu, J., Xu, S., Shum, K., Zhong, R., Song, J., & Zhang, T. (2024). RAGTruth: A hallucination corpus for developing trustworthy retrieval-augmented language models. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 10862–10878). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.acl-long.585
Particle Media. (n.d.). RAGTruth [Data set and source code]. GitHub. Retrieved July 29, 2026, from https://github.com/ParticleMedia/RAGTruth
Quach, V., Fisch, A., Schuster, T., Yala, A., Sohn, J. H., Jaakkola, T. S., & Barzilay, R. (2024). Conformal language modeling. In The Twelfth International Conference on Learning Representations. https://openreview.net/forum?id=pzUhfQ74c5
Rajpurkar, P., Jia, R., & Liang, P. (2018). Know what you don’t know: Unanswerable questions for SQuAD. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers) (pp. 784–789). Association for Computational Linguistics. https://doi.org/10.18653/v1/P18-2124
Romano, Y., Sesia, M., & Candès, E. J. (2020). Classification with valid and adaptive coverage. Advances in Neural Information Processing Systems, 33, 3581–3591. https://proceedings.neurips.cc/paper/2020/hash/244edd7e85dc81602b7615cd705545f5-Abstract.html
Ru, D., Qiu, L., Hu, X., Zhang, T., Shi, P., Chang, S., Cheng, J., Wang, C., Sun, S., Li, H., Zhang, Z., Wang, B., Jiang, J., He, T., Wang, Z., Liu, P., Zhang, Y., & Zhang, Z. (2024). RAGChecker: A fine-grained framework for diagnosing retrieval-augmented generation. Advances in Neural Information Processing Systems, 37. https://doi.org/10.52202/079017-0692
Saad-Falcon, J., Khattab, O., Potts, C., & Zaharia, M. (2024). ARES: An automated evaluation framework for retrieval-augmented generation systems. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) (pp. 338–354). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.naacl-long.20
Shuster, K., Poff, S., Chen, M., Kiela, D., & Weston, J. (2021). Retrieval augmentation reduces hallucination in conversation. In Findings of the Association for Computational Linguistics: EMNLP 2021 (pp. 3784–3803). Association for Computational Linguistics. https://doi.org/10.18653/v1/2021.findings-emnlp.320
Su, W., Chen, S., & Qian, E. (2026). Narrative-aware scientific claim verification agent with evidence ranking for ClimateCheck. Journal of Technology Informatics and Engineering, 5(1), 327–340. https://doi.org/10.51903/jtie.v5i1.549
Su, W., Chen, S., & Zhao, C. (2025). Budgeted multi-hop retrieval agent for compositional question answering: A retrieval-policy evaluation on the official MultiHop-RAG benchmark. Journal of Technology Informatics and Engineering, 4(3), 649–662. https://doi.org/10.51903/jtie.v4i3.543
Su, W., Rao, H., & Ma, E. (2026). Privacy and data-integrity risk cards for LLM agents: A UI/UX design framework for secure human oversight under prompt-injection attacks. International Journal of Graphic Design, 4(1), 186–191. https://doi.org/10.51903/ijgd.v4i1.3699
Sun, X., Lu, Y., & Chen, J. (2023). Controllable long-term user memory for multi-session dialogue: Confidence-gated writing, time-aware retrieval-augmented generation, and update/forgetting. Journal of Advanced Computing Systems, 3(8), 9–24. https://doi.org/10.69987/JACS.2023.30802
Sun, X., Zhong, Z. S., & Wu, Q. (2026). Retrieval-grounded HDFS log anomaly detection and deterministic failure narrative generation. Journal of Computational Systems and Applications, 3(1), 15–30. https://doi.org/10.64229/j6d7fr94
Tibshirani, R. J., Barber, R. F., Candès, E. J., & Ramdas, A. (2019). Conformal prediction under covariate shift. Advances in Neural Information Processing Systems, 32, 2530–2540. https://proceedings.neurips.cc/paper_files/paper/2019/hash/8fb21ee7a2207526da55a679f0332de2-Abstract.html
Vovk, V. (2012). Conditional validity of inductive conformal predictors. In Proceedings of the Asian Conference on Machine Learning (pp. 475–490). PMLR. https://proceedings.mlr.press/v25/vovk12.html
Wang, B., He, Y., Shui, Z., Xin, Q., & Lei, H. (2024). Predictive optimization of DDoS attack mitigation in distributed systems using machine learning. Applied and Computational Engineering, 64, 89–94. https://doi.org/10.54254/2755-2721/64/20241350
Xin, Q. (2025a). Explaining OpenStack failure-injection log anomalies with retrieved normal prototypes. Emerging Information Science and Technology, 6(2), 125–146. https://doi.org/10.18196/eist.v6i2.31232
Xin, Q. (2025b). Hybrid cloud architecture for efficient and cost-effective large language model deployment. Journal of Information Systems and Informatics, 7(3), 2182–2195. https://doi.org/10.51519/journalisi.v7i3.1170
Xin, Q. (2025c). Uncertainty-aware late fusion for 3D perception (confidence calibration + fusion rule learning). Journal of Technology Informatics and Engineering, 4(1), 215–238. https://doi.org/10.51903/jtie.v4i1.485
Xin, Q. (2026a). Auditable automated essay scoring and formative feedback: A rubric-grounded pipeline for secondary and higher education. Journal of Applied Artificial Intelligence in Education, 2(1), 1–19. https://doi.org/10.66053/jaaie.v2i1.348
Xin, Q. (2026b). Early-warning analytics with LLM intervention rationales for student retention decisions: Classroom interaction modeling with xAPI-edu-data and dropout/success prediction. Interdisciplinary Journal of Pedagogy and Research in Media Technology, 2(1), 9–27. https://doi.org/10.64268/inspire.v2i1.117
Xin, Q. (2026c). Explainable and fair credit risk scoring with counterfactual explanations: A reproducible evaluation on the German credit dataset (HELOC-motivated). Journal of Information and Technology, 14(2), 215–231. https://doi.org/10.32664/j-intech.v14i02.2228
Xin, Q. (2026d). Host-based intrusion detection with system call sequences: Window localization and forensic narratives. Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls, 8(2), 325–334. https://doi.org/10.28989/avitec.v8i2.3973
Xin, Q. (2026e). Log anomaly detection with conformal alert control and evidence-grounded incident ticket generation. Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls, 8(2), 247–264. https://doi.org/10.28989/avitec.v8i2.3974
Xin, Q. (2026f). Probabilistic bike-sharing demand forecasting under changing weather and seasonal regimes with transformer-based models. Findings. https://doi.org/10.32866/001c.157499
Xin, Q. (2026g). Self-supervised log anomaly detection with LogBERT-style transformers: Full empirical evaluation on a reproducible SynHDFS benchmark. Journal of Electrical Engineering and Computer Sciences, 11(1), 23–35. https://doi.org/10.54732/jeecs.v11i1.3
Xin, Q., Xu, Z., Guo, L., Zhao, F., & Wu, B. (2024). IoT traffic classification and anomaly detection method based on deep autoencoders. Applied and Computational Engineering, 69, 64–70. https://doi.org/10.54254/2755-2721/69/20241511
Xu, H., Chen, Y., & Med, A. (2025). Automatic detection and explanation of dark patterns from interface microcopy: Empirical comparison of BERT-style encoders, RoBERTa-style encoders, and LLM-style decoders on the ec-darkpattern dataset. Journal of Technology Informatics and Engineering, 4(3), 590–612. https://doi.org/10.51903/jtie.v4i3.491
Zhang, B., Rao, H., & Zhao, D. (2024). Evidence-grounded RAG for cloud-native DevOps: Hallucination-resistant AIOps question answering over private operations documents. Journal of Advanced Computing Systems, 4(3), 109–125. https://doi.org/10.69987/JACS.2024.40308
Zhang, B., Ren, Y., & Zou, J. (2025). LLM-style explainable e-commerce recommendation cards: A UI/UX design framework for trust-calibrated product recommendation. International Journal of Graphic Design, 3(2), 381–396. https://doi.org/10.51903/ijgd.v3i2.3697
Zhang, B., Sun, X., Liu, G., & Zhou, B. (2026). LLM-style DevOps copilot for cloud-native troubleshooting: Retrieval-augmented runbook generation and command-safety evaluation. Journal of Technology Informatics and Engineering, 5(2), 104–118. https://doi.org/10.51903/jtie.v5i2.534
Zhang, J. (2026). Early warning, grade prediction, and teacher-facing LLM-ready explanations toward an open volleyball course: Reproducible evidence from four public education datasets. Journal of Technology Informatics and Engineering, 5(2), 20–44. https://doi.org/10.51903/jtie.v5i2.525
Zhang, K., Chen, Y., & Qian, A. (2025). Evidence-grounded accounting disclosure review cards: A visual communication framework for LLM-style explanations over SEC financial statements and notes. International Journal of Graphic Design, 3(2), 395. https://doi.org/10.51903/ijgd.v3i2.3710
Zhang, R., Wen, Z., Wang, C., Tang, C., Xu, P., & Jiang, Y. (2025). Quality analysis and evaluation prediction of RAG retrieval based on machine learning algorithms. In 2025 5th International Conference on Computer Science, Electronic Information Engineering and Intelligent Control Technology (CEI) (pp. 1012–1018). IEEE. https://doi.org/10.1109/CEI66465.2025.11398480
Zhang, Y., & Zhang, H. (2025a). A therapist-facing session copilot for live counseling support: Reasoning-guided retrieval and ranking from multi-turn counseling dialogues. Journal of Technology Informatics and Engineering, 4(2), 464–486. https://doi.org/10.51903/jtie.v4i2.547
Zhang, Y., & Zhang, H. (2025b). Visualizing the right counseling support: Evidence-linked recommendation cards for explainable mental health intake interfaces. International Journal of Graphic Design, 3(1), 214–229. https://doi.org/10.51903/ijgd.v3i1.3722
Zhang, Y., & Zhou, Z. (2026). Strategy-aware therapist imitation for emotional support dialogues: A reproducible ESConv study for LLM response control. Advances in Educational Technology and Psychology, 10(2), 92–97. https://doi.org/10.23977/aetp.2026.100213
Zhao, S., Bai, J., & Roberson, D. (2025). Multi-horizon GPU demand forecasting with workload semantics and operational risk curves: An empirical study on Alibaba Clusterdata GPU trace. Journal of Technology Informatics and Engineering, 4(3), 544–571. https://doi.org/10.51903/jtie.v4i3.498
Zheng, D., & Li, C. (2024). Behavior-level jailbreak resistance via multi-stage refusal + utility preservation. Journal of Advanced Computing Systems, 4(1), 83–99. https://doi.org/10.69987/JACS.2024.40107
Zheng, D., Li, C., & Davidson, H. (2023). Continual red-teaming for in-the-wild jailbreaks via online guardrail updates and guardrail distillation. Journal of Advanced Computing Systems, 3(2), 35–49. https://doi.org/10.69987/JACS.2023.30203
Zheng, D., Zhang, B., & Geibel, J. (2024). VerifySafe: Toxicity-safe agent responses under adversarial prompts with evidence-based self-verification. Journal of Advanced Computing Systems, 4(1), 67–82. https://doi.org/10.69987/JACS.2024.40106
Zhong, Z. S., & Ling, S. (2024a). Improved theoretical guarantee for rank aggregation via spectral method. Information and Inference: A Journal of the IMA, 13(3), Article iaae020. https://doi.org/10.1093/imaiai/iaae020
Zhong, Z. S., & Ling, S. (2024b). Uncertainty quantification of spectral estimator and MLE for orthogonal group synchronization [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2408.05944
Zhong, Z. S., Chen, J., Zhong, E., & Sun, X. (2025). Evidence-calibrated RAG for unanswerable question answering: Retrieval coverage, abstention calibration, and hallucination-proxy analysis on SQuAD 2.0. Journal of Technology Informatics and Engineering, 4(2), 502–520. https://doi.org/10.51903/jtie.v4i2.536
Zhong, Z. S., Li, C., & Rao, H. (2026). Trajectory reliability prediction for generalist AI agents: Tool-use failure analysis and success forecasting on ZClawBench. Journal of Technology Informatics and Engineering, 5(1), 341–360. https://doi.org/10.51903/jtie.v5i1.539
Zhong, Z. S., Pan, X., & Lei, Q. (2025). Bridging domains with approximately shared features. In Proceedings of the 28th International Conference on Artificial Intelligence and Statistics (Vol. 258, pp. 559–567). PMLR. https://proceedings.mlr.press/v258/zhong25a.html
Zhong, Z. S., Wu, Q., & Mi, G. (2025). Uncertainty-aware medical image explanation cards: LLM-generated visual explanations for AI-assisted radiology interfaces. International Journal of Graphic Design, 3(2), 415–436. https://doi.org/10.51903/ijgd.v3i2.3616
Zhou, B., Jin, J., & Zhao, D. (2025). Calibrated resume-job matching for trustworthy LLM-assisted recruiter screening: Pairwise matching, probability calibration, and selective refusal on two public recruitment datasets. Journal of Technology Informatics and Engineering, 4(3), 625–648. https://doi.org/10.51903/jtie.v4i3.529
Zhou, B., Li, C., & Liu, L. (2025). Risk-calibrated patient-facing AI safety cards: A UI/UX design framework for rubric-based medical risk communication. International Journal of Graphic Design, 3(2), 365–380. https://doi.org/10.51903/ijgd.v3i2.3696
Zhou, S., Chen, Y., & Lee, K. (2026). Accounting-aware evidence-constrained agents for disclosure, settlement, and secondary-market risk monitoring in tokenized assets. Journal of Technology Informatics and Engineering, 5(2), 60–74. https://doi.org/10.51903/jtie.v5i2.544
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