This paper proposes a RAG-based pipeline that generates goals, tasks, activities, and requirements grounded in existing administrative documents and expert-authored descriptions to support the semi-automatic generation and harmonization of structured job description components.
ReSB2 is introduced, a framework for retrieving and linking similar legislative bills that supports human–machine collaboration and helps reduce redundancy in the lawmaking process, and fine-tunes two ModernBERT-based language models on authentic legislative data.
Lucas G. L. Costa, Átila Souza, Elves Rodrigues et al.· Proceedings of the 37th ACM...· 0 citations
It is suggested that TnT-LLM and CLIMB can be used in practical situations in the SE domain, while researchers should first assess the complexity of the generated taxonomies and their cost using a subset of the target data to decide whether to use automated methods or human experts.
Sota Nakashima, Yuta Ishimoto, Masanari Kondo et al.· 0 citations
This research affirms the necessity of a human-in-the-loop paradigm in the EA discipline within this specific context and suggests that LLMs function effectively as augmentation accelerators rather than human replacements, thereby shifting the enterprise architect's focus from manual drafting to analytical validation a...
R. Darmawan, Alfa Yohannis· Proceeding of International...· 0 citations
A practical, quality-assured method for publishing KOS-related RFPs as context-bearing registry records is contributed by integrating a two-track pipeline—scripted page generation for structured fields and generative summarization for narrative RFP text—with HITL governance for expert verification and correction.
This work introduces GB/T-Bench, the first benchmark for the structured review of national standard documents, and proposes GB/T-Reviewer, a multi-agent framework that converts review knowledge into specialized skills and coordinates global inspection, targeted diagnosis, rule scanning, and result verification, paving...
Tao Wang, Qihao Yang, Rongjiao Liang et al.· 0 citations
The proposed FlowGen uses LLM-based Semantic Information Processing (SIP) to extract semantic elements, constructs a Semantic Relational Graph (SRG) encoded by an enhanced R-GAT for basic flow generation (BFGen), and further supports branch point prediction through BPP and branch-conditioned alternative flow generation...
Guang-Yu Wang, Bangqi Li, Ji Wu et al.· 0 citations
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