用于工业过程预测的大语言模型引导的任务语义场分解
LLM-Guided Task-Semantic Field Factorization for Industrial Process Forecasting
- College of Information Science and Engineering, Northeastern University（东北大学信息科学与工程学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对工业过程预测中标记数据稀缺等问题，提出大语言模型引导的任务语义场分解框架TSF，通过构建任务语义场，结合传统时间序列主干进行训练和推理，在多任务上降低平均绝对误差，增加参数少且推理开销小。
AI中文摘要：
流程工业依赖时间序列预测和软传感来估计难以在线测量的质量变量。标记数据稀缺，操作模式频繁变化，为每种情况重新训练模型或重建对齐管道成本高昂。本文提出了任务语义场分解（TSF），这是一个大语言模型引导的框架。TSF在训练前从任务协议和变量文档构建任务语义场，仅将大语言模型用于离线语义构建。在线训练和推理仍使用传统时间序列主干。在多个复杂工业预测和软传感任务上，TSF在改进设置下平均将平均绝对误差降低6.4%，最大降幅达25.5%。它仅增加约1800 - 3000个参数，额外在线推理开销小于0.008毫秒/步。这些结果表明TSF将现有过程文档转化为跨主干和语义生成器的可测量预测增益，同时保持轻量级以便部署。
英文摘要：
Process industries rely on time-series forecasting and soft sensing to estimate quality variables that are hard to measure online. Labeled data are scarce, operating regimes change frequently, and retraining models or rebuilding alignment pipelines for each scenario is costly. Such settings often provide variable tables and process documents that record variable names, units, physical meanings, and process roles. However, standard time-series backbones usually treat inputs as anonymous numerical columns. Existing text-enhanced methods also rarely make the semantic-logical relations between input variables and the prediction target available to the model within each numerical window. To address this problem, this article proposes Task-Semantic Field Factorization (TSF), a large language model (LLM)-guided framework. TSF builds a task-semantic field from task protocols and variable documents before training and uses the LLM only for offline semantic construction. Online training and inference are handled by conventional time-series backbones. During training and inference, the current numerical window activates variable semantics, so semantic information participates in each prediction and supports adaptation to different prediction targets and operating shifts. Across multiple complex industrial forecasting and delayed soft-sensing tasks, TSF reduces MAE by 3.6\% on average. Across all dataset--backbone pairs, the macro-average reduction is 2.9\%, with a maximum reduction of 24.9\%. It adds only about 0.7--4.3k parameters, with less than 8\,$μ$s/sample of additional online inference overhead. These results show that TSF turns existing process documents into measurable forecasting gains across backbones and semantic generators while remaining lightweight for deployment.
