Automated Extraction of Records of Processing Activities (RoPA) Using Hybrid RAG and Locally Deployed Large Language Models
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Computer Science > Computation and Language
arXiv:2609.27359 (cs)
[Submitted on 23 Sep 2026]
Title:Automated Extraction of Records of Processing Activities (RoPA) Using Hybrid RAG and Locally Deployed Large Language Models
View a PDF of the paper titled Automated Extraction of Records of Processing Activities (RoPA) Using Hybrid RAG and Locally Deployed Large Language Models, by To Duy Hinh and 3 other authors
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Abstract:Vietnam's Personal Data Protection Law (Law No. 91/2025/QH15) and Decree No. 356/2025/ND-CP, effective January 1, 2026, require organizations to establish and maintain Records of Processing Activities (RoPA). Manual RoPA preparation is labor-intensive, while cloud-hosted large language models (LLMs) may conflict with data-sovereignty requirements. We propose RoPA Manager, a system for automated RoPA information extraction using hybrid retrieval that combines lexical ranking over tsvector, dense-vector search, Reciprocal Rank Fusion (RRF), and locally deployed LLMs. We introduce a Vietnamese RoPA benchmark with 32 organizations, 77 processing activities, 12 field groups, and 4,338 reference values. Evaluation is reported at three distinct levels. The automated scorer, tested on perturbed data without invoking an LLM, achieved F1 = 0.9493 [0.9436, 0.9548]; this measures scorer robustness rather than end-to-end extraction accuracy. End-to-end extraction achieved token coverage of 50.04-55.25% against the reference labels. Two independent experts reviewed 1,558 reference values (35.9% of the benchmark), found no incorrect values, and achieved 99.68% agreement with PABAK = 0.9936. Value-level precision was not measured. Across 32 paired scenarios on a 24 GB GPU, locally deployed Qwen3.5-27B-GPTQ-Int4 showed no statistically significant difference from cloud-based DeepSeek-V4-Flash (difference 0.20 percentage points in favor of DeepSeek, 95% CI [-0.93, 1.32], p = 0.72), while Gemma-4-31B performed significantly worse (p < 0.01).
| Comments: | English version followed by Vietnamese version. Accepted for publication in the Proceedings of the 29th National Conference on Selected Issues of Information and Communication Technology (VNICT 2026), Hanoi, Vietnam, November 7-8, 2026 |
| Subjects: | Computation and Language (cs.CL); Cryptography and Security (cs.CR); Information Retrieval (cs.IR) |
| ACM classes: | I.2.7; H.3.3; H.3.1 |
| Cite as: | arXiv:2609.27359 [cs.CL] |
| (or arXiv:2609.27359v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.27359
arXiv-issued DOI via DataCite (pending registration)
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