摘要：

领域大语言模型(以下简称“大模型”)被视为推动工业智能化转型的关键技术，而通用大模型作为其研发起点，在高可靠性要求的垂直场景中仍面临知识深度不足与“幻觉”风险等根本性挑战。为系统评估通用大模型在天然气管道这一典型高危工业领域的实际能力，并为后续专用模型的研发与部署提供科学依据，本文构建了面向该领域的标准化评测基准 PipeMind-Bench，涵盖5大能力维度、36个细分技术方向，包含1580道具有确定答案的题目，并通过多组提示词设计探究上下文引导对模型性能的影响。基于该基准，我们对16个国内主流大模型开展了系统性评测，得出以下关键发现：(1)模型类型与参数规模并非性能决定因素：顶尖开源与闭源模型整体表现相当，推理型模型并未显著优于非推理型模型；部分中等规模模型(如14 B、32 B)在准确率、响应效率与部署成本之间实现良好平衡，是当前领域应用的高性价比选择。(2)任务能力高度不均衡：模型在“天然气基础知识”等通用性强的任务上表现较好，但在工艺流程、设备规范、安全标准等高度专业化子领域的性能显著下降。该结果为数据资源的差异化投入提供了实证依据——基础任务可减少训练数据冗余，而薄弱专业方向亟需高质量标注数据的定向补充。(3)推理与计算能力存在结构性短板：尽管问答能力相对稳健，但模型在逻辑推理和数值计算方面明显不足。其中，推理能力可通过领域数据微调等方式提升，但受限于架构对数值运算的原生支持薄弱，计算能力难以通过常规训练有效增强，建议在工程实践中将复杂数值任务交由专用计算模块处理。(4)幻觉问题集中于3类高风险错误：事实矛盾、事实编造与逻辑错误是模型在油气管道任务中最常见的幻觉形式，严重威胁决策安全性。未来领域模型训练应优先构建覆盖此类风险的高置信度数据集，并结合约束生成、检索增强或事后校验机制，实现对关键幻觉类型的精准抑制。本研究表明，当前通用大模型尚难以满足天然气管道等高危工业场景对可靠性与专业性的严苛要求，结构化知识注入与任务解耦设计应成为下一阶段研发的核心方向。所提出的评估体系不仅为行业模型选型、微调与迭代提供了可复现的基准，也为其他关键基础设施领域的AI可信应用探索提供了方法论参考。

关键词: 生成式大语言模型, 天然气管道, 评测基准, 领域适应性, 提示工程

Abstract:

The domain-specific large language models (LLMs) are recognized as a key technology for driving the intelligent transformation of industries. However, general-purpose LLMs, as their starting point, still face fundamental challenges in vertical scenarios with high-reliability requirements, such as insufficient depth of knowledge and the risk of hallucinations. To systematically evaluate the practical capabilities of general-purpose LLMs in typical high-risk industrial domains such as natural gas pipelines, and to provide a scientific basis for the subsequent development and deployment of specialized models, this paper constructs a standardized evaluation benchmark for the domain, named PipeMind-Bench. It covers 5 major capability dimensions and 36 fine-grained technical areas, comprising 1,580 questions with definitive answers. Through multiple prompt designs, it investigates the impact of contextual guidance on model performance. Based on this benchmark, we conducted a systematic evaluation of 16 mainstream domestic LLMs, yielding the following key findings: (1) Model type and parameter scale are not decisive factors for performance: Top open-source and closed-source models exhibit comparable overall performance, and reasoning-specialized models do not significantly outperform non-reasoning-specialized models. Some medium-scale models (e.g., 14 B, 32 B) achieve a good balance between accuracy, response efficiency, and deployment costs, representing a cost-effective choice for current domain applications. (2) Task capabilities are highly imbalanced: Models perform well on general tasks like “fundamentals of natural gas,” but their performance declines significantly in highly specialized subfields such as process design, equipment specifications, and safety standards. This finding provides empirical support for differentiated investment in data resources: reducing training data redundancy for foundational tasks while prioritizing the targeted supplementation of high-quality annotated data in weak specialized areas. (3) Structural shortcomings exist in reasoning and computational capabilities: Although question-answering capabilities remain relatively robust, models exhibit clear deficiencies in logical reasoning and numerical computation. Reasoning ability can be partially improved through domain-specific fine-tuning. However, constrained by the inherent weakness of the architecture in supporting numerical operations, computational capability is difficult to enhance effectively through conventional training. Thus, it is recommended that complex numerical tasks be delegated to dedicated computational modules in engineering practice. (4) Hallucination issues concentrate on three high-risk error types: Factual contradictions, fabrication of facts, and logical errors constitute the most common forms of hallucination in oil and gas pipeline tasks, posing serious threats to decision-making safety. Future training of domain-specific models should prioritize the construction of high-confidence datasets covering these risks, combined with mechanisms such as constrained generation, retrieval augmentation, or post-hoc verification, to achieve precise suppression of critical hallucination types. This study reveals that current general-purpose LLMs are still inadequate to meet the stringent demands for reliability and expertise in high-risk industrial scenarios like natural gas pipelines. Structured knowledge injection and task-decoupled design should become the core directions for the next phase of research and development. The proposed evaluation framework not only provides a reproducible benchmark for model selection, fine-tuning, and iteration in the industry but also offers methodological references for exploring trustworthy AI applications in other critical infrastructure domains.

Key words: generative large language models, natural gas pipelines, evaluation benchmarks, domain adaptability, prompt engineering