LMT: A Bayesian Framework for Causal Discovery from Textual Alarm Records in Manufacturing Systems
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LMT：从制造系统文本告警记录发现因果关系的贝叶斯框架
- 研究问题
- 如何结合制造系统告警的文本语义与时间戳，发现事件类型之间的因果关系，并减少仅凭语言模型语义误认相关性的风险？
- 方法与思路
- 作者先用大语言模型提取语义因果信号并构造图先验，再利用基于泊松过程的时间戳似然更新先验，形成贝叶斯因果发现框架。
- 材料陈述的贡献
- 摘要称，文本和时序证据的结合可生成可解释且有数据支持的因果图，模拟展示多种设定下的效果，尤其适用于小样本告警场景。
- 阅读关注点
- 应核对图边获得因果解释所需的假设、未观测共同原因和反馈的处理，以及语言模型先验误设与泊松似然误设的影响。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收均未核实；摘要未说明因果识别条件，语义与时间先后证据本身不足以保证因果有效性。
原文摘要
Textual event records, such as alarm logs, have become an increasingly common data source in engineering and manufacturing systems. Beyond identifying correlations or recurring patterns, engineers are often interested in understanding which types of events causally trigger or influence other events during system operation. Textual event descriptions may contain semantic clues about such causal relationships, and recent large language models (LLMs) provide a promising tool for extracting these signals. However, relying solely on LLM-encoded textual information is insufficient for accurate causal discovery, since semantic patterns do not directly reveal causal mechanisms and may confuse causation with correlation or frequent sequential patterns. To address these challenges, we propose \textbf{LMT}, a Bayesian causal discovery framework for engineering event data that jointly leverages textual descriptions and timestamps. Specifically, LMT first uses LLMs to extract semantic causal signals from event descriptions and constructs a prior distribution over causal graphs among event types or event clusters. It then incorporates temporal evidence through a Poisson-process-based likelihood, allowing the LLM-informed prior to be refined by timestamp-based statistical evidence. By integrating the textual and temporal information, LMT produces a causal graph that is both interpretable and data-supported. Simulation studies show that the proposed framework is effective across different settings and is especially advantageous in small-sample alarm-event scenarios.