Retrieval Error or Generation Error? Hierarchical Failure Attribution and Evidence-Constrained Correction for Legal LLM-RAG
DOI:
https://doi.org/10.61424/besja393Keywords:
Legal retrieval-augmented generation, legal information retrieval, failure attribution, evidence-constrained correction, citation support, selective prediction, Legal RAG BenchAbstract
Legal retrieval-augmented generation (RAG) can fail because the supporting authority was not retrieved, because retrieved authority was not selected as evidence, or because selected evidence did not produce an adequate answer. Treating these outcomes as one accuracy score obscures the intervention required. This study evaluates those failure sources on Legal RAG Bench, comprising 4,876 passages from the Victorian Criminal Charge Book and 100 expert-written questions with long-form reference answers and passage-level relevance labels. Four retrievers—BM25, word TF-IDF, character TF-IDF, and reciprocal-rank fusion (RRF)—were crossed with three deterministic evidence-selection policies in 12 conditions, producing 1,200 question-condition observations. BM25 achieved the highest exact annotated-passage Hit@5 (0.34), whereas RRF paired with Query-Focused selection produced the highest answer token F1 (0.2308). Under a strict hierarchy applied to all observations, 74.25% were annotated-passage retrieval misses, 9.92% were evidence-selection failures, 2.58% were answer-realization failures, and 13.25% were strictly supported successes. An evidence-constrained correction gate increased mean token F1 from 0.2043 to 0.2184 and ROUGE-L from 0.1425 to 0.1580. The question-paired token-F1 improvement was statistically significant (Wilcoxon p = .0213; mean difference = 0.0141, 95% bootstrap CI [0.0031, 0.0267]), although the lexical citation-support proxy decreased slightly from 0.9493 to 0.9448. The lexical groundedness proxy equaled 1.0000 because every answer consisted only of sentences selected from its cited passages. In the lowest lexical-overlap quartile, every retriever had Hit@5 = 0, identifying vocabulary mismatch as the principal unresolved bottleneck. The results support component-specific legal RAG evaluation, retrieval-aware correction, and abstention policies that expose residual risk.
References
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. Informatics, Electrical and Electronics Engineering, 6(1), 28–43. https://doi.org/10.33474/infotron.v6i1.24923
Bai, J., Wang, H., Wu, Q., & Zhang, B. (2026). Privacy-robust incrementality estimation in cookieless settings via uplift modeling: Reproducible evidence from the Hillstrom e-mail experiment. Journal of Technology Informatics and Engineering, 5(1), 17–38. https://doi.org/10.51903/jtie.v5i1.468
Bai, J., & Wu, Q. (2026). Privacy-safe marketing mix modeling and budget optimization under identifier loss: A controlled simulation study. International Journal of Electronics and Communications Systems, 6(1), 83–95. https://doi.org/10.24042/ijecs.v6i1.30533
Bhattacharya, P., Ghosh, K., Ghosh, S., Pal, A., Mehta, P., Bhattacharya, A., & Majumder, P. (2019). Overview of the FIRE 2019 AILA track: Artificial intelligence for legal assistance. In Working notes of FIRE 2019—Forum for Information Retrieval Evaluation (pp. 1–12). CEUR-WS.org. https://ceur-ws.org/Vol-2517/T1-1.pdf
Butler, A.-R., & Butler, U. (2026). Legal RAG Bench: An end-to-end benchmark for legal RAG [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2603.01710
Butler, U., Butler, A.-R., & Malec, A. L. (2025). The Massive Legal Embedding Benchmark (MLEB) [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2510.19365
Chalkidis, I., Jana, A., Hartung, D., Bommarito, M., Androutsopoulos, I., Katz, D. M., & Aletras, N. (2022). LexGLUE: A benchmark dataset for legal language understanding in English. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 4310–4330). Association for Computational Linguistics. https://doi.org/10.18653/v1/2022.acl-long.297
Chang, X., Lu, Y., & Zhong, Z. S. (2026). Review-grounded explainable recommendation with faithfulness evaluation on Amazon Reviews. JEECS (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., 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
Dahl, M., Magesh, V., Suzgun, M., & Ho, D. E. (2024). Large legal fictions: Profiling legal hallucinations in large language models. Journal of Legal Analysis, 16(1), 64–93. https://doi.org/10.1093/jla/laae003
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
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
Guha, N., Nyarko, J., Ho, D. E., Ré, C., Chilton, A., Narayana, A., Chohlas-Wood, A., Peters, A., Waldon, B., Rockmore, D. N., Zambrano, D., Talisman, D., Hoque, E., Surani, F., Fagan, F., Sarfaty, G., Dickinson, G. M., Porat, H., Hegland, J., . . . Li, Z. (2023). LegalBench: A collaboratively built benchmark for measuring legal reasoning in large language models. In Advances in Neural Information Processing Systems (Vol. 36). https://proceedings.neurips.cc/paper_files/paper/2023/hash/89e44582fd28ddfea1ea4dcb0ebbf4b0-Abstract-Datasets_and_Benchmarks.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 (Vol. 70, 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., Nie, J., & Li, C. (2026). Power-aware inventory planning for AI infrastructure using job-level forecasting and LLM workload explanations. Journal of Technology Informatics and Engineering, 5(1), 341–359. https://doi.org/10.51903/jtie.v5i1.548
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
Isaacus. (2026). Legal RAG Bench [Data set]. Hugging Face. https://huggingface.co/datasets/isaacus/legal-rag-bench
Jin, J. (2025a). 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
Jin, J. (2025b). 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. (2025c). 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. (2024). 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
Judicial College of Victoria. (n.d.). Victorian criminal charge book. Retrieved July 29, 2026, from https://resources.judicialcollege.vic.edu.au/article/1053858
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
Karpukhin, V., Oguz, B., Min, S., Lewis, P., Wu, L., Edunov, S., Chen, D., & Yih, W.-t. (2020). Dense passage retrieval for open-domain question answering. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (pp. 6769–6781). Association for Computational Linguistics. https://doi.org/10.18653/v1/2020.emnlp-main.550
Khattab, O., & Zaharia, M. (2020). ColBERT: Efficient and effective passage search via contextualized late interaction over BERT. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 39–48). Association for Computing Machinery. https://doi.org/10.1145/3397271.3401075
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. In Advances in Neural Information Processing Systems (Vol. 33, pp. 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, 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, 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, N. F., Zhang, T., & Liang, P. (2023). Evaluating verifiability in generative search engines. In Findings of the Association for Computational Linguistics: EMNLP 2023 (pp. 7001–7025). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.findings-emnlp.467
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
Magesh, V., Surani, F., Dahl, M., Suzgun, M., Manning, C. D., & Ho, D. E. (2025). Hallucination-free? Assessing the reliability of leading AI legal research tools. Journal of Empirical Legal Studies, 22(2), 216–242. https://doi.org/10.1111/jels.12413
Malaviya, C., Lee, S., Chen, S., Sieber, E., Yatskar, M., & Roth, D. (2024). ExpertQA: Expert-curated questions and attributed answers. 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. 3025–3045). Association for Computational Linguistics. https://doi.org/10.18653/v1/2024.naacl-long.167
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
Min, S., Krishna, K., Lyu, X., Lewis, M., Yih, W.-t., Koh, P., 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
Mu, J., Lu, Y., & Smith, M. (2023). LLM-assisted incrementality (uplift) modeling for email advertising: From feature interactions to interpretable audience–creative–channel policies. Journal of Advanced Computing Systems, 3(1), 31–48. https://doi.org/10.69987/JACS.2023.30103
Muennighoff, N., Tazi, N., Magne, L., & Reimers, N. (2023). MTEB: Massive text embedding benchmark. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics (pp. 2014–2037). Association for Computational Linguistics. https://doi.org/10.18653/v1/2023.eacl-main.148
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
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
Nogueira, R., & Cho, K. (2019). Passage re-ranking with BERT [Preprint]. arXiv. https://doi.org/10.48550/arXiv.1901.04085
Pipitone, N., & Houir Alami, G. (2024). LegalBench-RAG: A benchmark for retrieval-augmented generation in the legal domain [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2408.10343
Rashkin, H., Nikolaev, V., Lamm, M., Aroyo, L., Collins, M., Das, D., Petrov, S., Tomar, G. S., Turc, I., & Reitter, D. (2023). Measuring attribution in natural language generation models. Computational Linguistics, 49(4), 777–840. https://doi.org/10.1162/coli_a_00486
Robertson, S., & Zaragoza, H. (2009). The probabilistic relevance framework: BM25 and beyond. Foundations and Trends in Information Retrieval, 3(4), 333–389. https://doi.org/10.1561/1500000019
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. In Advances in Neural Information Processing Systems (Vol. 37, pp. 21999–22027). 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
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
Thakur, N., Reimers, N., Rücklé, A., Srivastava, A., & Gurevych, I. (2021). BEIR: A heterogeneous benchmark for zero-shot evaluation of information retrieval models. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks (Vol. 1). https://openreview.net/forum?id=wCu6T5xFjeJ
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(1), 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 (AVITEC), 8(2), 325–334. https://doi.org/10.28989/avitec.v8i2.3973
Xin, Q. (2026e). LiDAR–camera object-level fusion for multi-target tracking using JPDA and EKF: A reproducible empirical study on a PandaSet-parameterised five-sequence dataset. Journal of Technology Informatics and Engineering, 5(1), 54–76. https://doi.org/10.51903/jtie.v5i1.486
Xin, Q. (2026f). Log anomaly detection with conformal alert control and evidence-grounded incident ticket generation. Aviation Electronics, Information Technology, Telecommunications, Electricals, and Controls (AVITEC), 8(2), 247–264. https://doi.org/10.28989/avitec.v8i2.3974
Xin, Q. (2026g). 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. (2026h). Self-supervised customer representation learning for segmentation and next-purchase prediction on UCI Online Retail. Journal of Information and Technology, 14(1), 20–37. https://doi.org/10.32664/j-intech.v14i01.2229
Xin, Q. (2026i). Self-supervised log anomaly detection with LogBERT-style transformers: Full empirical evaluation on a reproducible SynHDFS benchmark. JEECS (Journal of Electrical Engineering and Computer Sciences), 11(1), 23–35. https://doi.org/10.54732/jeecs.v11i1.3
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, 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 [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2511.19481
Zhang, Y., & Zhang, H. (2025). 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., & 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
Zhao, S., Ren, Y., & Chang, X. (2026). Profit-aware spot GPU admission control with cost-sensitive loss and evidence-grounded policy memos for AI workload supply-demand matching. Journal of Technology Informatics and Engineering, 5(2), 45–59. https://doi.org/10.51903/jtie.v5i2.545
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
Zheng, L., Guha, N., Arifov, J., Zhang, S., Skreta, M., Manning, C. D., Henderson, P., & Ho, D. E. (2025). A reasoning-focused legal retrieval benchmark. In Proceedings of the 2025 Symposium on Computer Science and Law (pp. 169–193). Association for Computing Machinery. https://doi.org/10.1145/3709025.3712219
Zhong, H., Xiao, C., Tu, C., Zhang, T., Liu, Z., & Sun, M. (2020). JEC-QA: A legal-domain question answering dataset. Proceedings of the AAAI Conference on Artificial Intelligence, 34(5), 9701–9708. https://doi.org/10.1609/aaai.v34i05.6519
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., & 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., 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). Proceedings of Machine Learning Research. 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., 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, H., & Zhang, K. (2025). News-based uncertainty and macro-market fusion for VIX direction forecasting: Evidence from 2015–2024 FRED panel. Journal of Technology Informatics and Engineering, 4(2), 487–501. https://doi.org/10.51903/jtie.v4i2.540
Zhou, S., Chen, Y., & Lee, K. (2026). Accounting-aware evidence-constrained agents for disclosure, settlement, and secondary-market risk monitoring in tokenized RWA infrastructure. Journal of Technology Informatics and Engineering, 5(2), 60–74. https://doi.org/10.51903/jtie.v5i2.544
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Adeline Zhou (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
