English(EN) Automated Event Log Generation from Unstructured Text Using Finetuned LLMs

微调大语言模型以从文本自动生成流程挖掘日志

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 14:39

研究人员开发了一个新的框架，使用大语言模型（LLMs）从非结构化文本自动生成结构化事件日志。该方法解决了手动创建事件日志的瓶颈问题，而事件日志对于流程挖掘至关重要。通过在自定义文本到日志数据集上微调LLMs，模型可以从事件单和报告等来源提取高保真事件数据，其性能远超零样本或少样本提示。 AI

影响自动化流程挖掘的结构化数据创建，有可能解锁大量先前无法使用的组织知识。

排序理由该条目描述了一篇研究论文，其中详细介绍了一种使用LLMs生成事件日志的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

本文如何被排名

Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该条目描述了一篇研究论文，其中详细介绍了一种使用LLMs生成事件日志的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration: Single-source cluster; Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics: paper, infra; Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance: High; Clearly on-topic for AI-industry coverage.
Story freshness: 29 days old; Aged out of breaking-news scoring windows; ranking reflects the durable signal from the full source set.

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Hugging Face Daily Papers TIER_1 English(EN) · 2026-09-01 14:39

使用微调大语言模型从非结构化文本自动生成事件日志

Process mining (PM) provides a powerful framework for discovering and optimizing operational processes from event data. However, the efficacy of PM techniques is strictly predicated on the availability of structured event logs. Thus far, event logs have often been laboriously cre…