用三值评分法让大模型生成的需求既合规又可解释。
Model-Driven Requirements Configuration with Three-Valued Uncertainty Scoring
- 用符号验证层确保需求结构正确，大模型只负责生成
- 94.6%的需求案例完全消除结构错误，仅0.39%有少量遗留问题
- 引入真/不确定/假三值体系，量化大模型决策的不确定性
大型语言模型（LLMs）在自动化需求获取中具备自然语言灵活性，但常生成结构无效或逻辑矛盾的需求，缺乏形式化正确性保障。本研究旨在消除逻辑不一致并强制保证LLM生成需求的结构合规性，同时量化预验证阶段的决策不确定性。提出一种神经符号多智能体架构，实现面向需求编写与管理的面向对象方法（OOMRAM）格结构。LLM作为非确定性启发式进行格遍历，符号验证器则强制执行所有结构约束。引入三值（真、不确定、假）分类框架，对需求决策进行前后验证评分。在11个应用领域共37个自然语言项目愿景上评估，35例完全消除结构不一致（94.6%），剩余两例仅剩6处未解决结构错误（占总决策0.39%，因迭代限制）。三值分析显示24.7%的决策处于不确定状态——即结构合法但未被利益相关方明确要求。结论表明，将结构完整性交由确定性符号层处理可保证结构合规；三值分类提供了一种正式度量神经不确定性的方式，有助于在正式需求工程中安全部署大模型。
原文摘要 · Abstract (English)
Context: Large Language Models (LLMs) offer natural-language flexibility for automated requirements elicitation but frequently generate structurally invalid requirements and logical inconsistencies, lacking formal correctness guarantees. Objectives: This study aims to eliminate logical inconsistencies and enforce structural conformance in LLM-generated requirements while quantifying the LLM's pre-validation decision uncertainty within a formal domain model. Methods: We present a neuro-symbolic multi-agent architecture that operationalizes the Object-Oriented Method for Requirements Authoring and Management (OOMRAM) lattice. The LLM acts as a non-deterministic heuristic for lattice traversal, while a deterministic symbolic validator enforces all structural constraints. We introduce a three-valued (T, I, F) -- Truth, Indeterminacy, Falsity -- framework to classify and score the LLM's requirement decisions before and after validation. Results: Evaluated across 37 natural-language project visions in eleven application families, the system completely eliminated structural inconsistencies in 35 out of 37 cases (94.6%), with the remaining two containing only 6 unresolved structural errors (0.39% of decisions) due to iteration limits. Three-valued analysis revealed that 24.7% of all decisions are indeterminate -- structurally valid but discretionary choices not explicitly mandated by the stakeholder. Conclusion: Offloading structural integrity to a deterministic symbolic layer successfully guarantees structural conformance, while the three-valued classification provides a formal way to measure neural uncertainty, facilitating safe LLM deployment in formal requirements engineering.
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