Modern high-stakes systems, such as healthcare or robotics, often generate vast streaming event sequences. Our goal is to design an efficient, plug-and-play tool to elicit logic tree-based explanations from Large Language Models (LLMs) to provide customized insights into each observed event sequence. Built on the temporal point process model for events, our method employs the likelihood function as a score to evaluate generated logic trees. We propose an amortized Expectation-Maximization (EM) learning framework and treat the logic tree as latent variables. In the E-step, we evaluate the posterior distribution over the latent logic trees using an LLM prior and the likelihood of the observed event sequences. LLM provides a high-quality prior for the latent logic trees, however, since the posterior is built over a discrete combinatorial space, we cannot get the closed-form solution. We propose to generate logic tree samples from the posterior using a learnable GFlowNet, which is a diversity-seeking generator for structured discrete variables. The M-step employs the generated logic rules to approximate marginalization over the posterior, facilitating the learning of model parameters and refining the tunable LLM prior parameters. In the online setting, our locally built, lightweight model will iteratively extract the most relevant rules from LLMs for each sequence using only a few iterations. Empirical demonstrations showcase the promising performance and adaptability of our framework.

翻译：现代高风险系统（如医疗或机器人领域）常产生海量流式事件序列。本研究旨在设计一种高效即插即用的工具，从大语言模型（LLMs）中提取基于逻辑树的解释，从而为每个观测到的事件序列提供定制化洞察。该方法建立在事件的时间点过程模型基础上，利用似然函数作为评估生成逻辑树的评分标准。我们提出一种摊销期望最大化（EM）学习框架，将逻辑树视为潜变量。在E步中，我们使用LLM先验分布和观测事件序列的似然函数来评估潜逻辑树的后验分布。LLM为潜逻辑树提供了高质量的先验，但由于后验建立在离散组合空间上，无法获得闭式解。我们提出使用可学习的GFlowNet从后验分布中生成逻辑树样本——这是一种针对结构化离散变量的多样性生成器。M步利用生成的逻辑规则近似后验边缘化，促进模型参数学习并优化可调整的LLM先验参数。在在线场景中，我们本地构建的轻量级模型仅需少量迭代即可从LLMs中为每个序列迭代提取最相关的规则。实证研究表明，该框架具有优异的性能和良好的适应性。