Leveraging Large Language Models Llms For Process Mining Technical Abstract: this technical report describes the intersection of process mining and large language models (llms), specifically focusing on the abstraction of traditional and object centric process mining artifacts into textual format. we introduce and explore various prompting strategies: direct answering, where the large language model directly. This technical report describes the intersection of process mining and large language models (llms), specifically focusing on the abstrac tion of traditional and object centric process mining artifacts into textual format. we introduce and explore various prompting strate gies: direct answering, where the large language model directly.
Leveraging Large Language Models Llms For Process Mining Technical This paper reviews the current implementations of llms in pm and reflects on three different questions. 1) what is the minimal set of capabilities required for pm on llms? 2) which benchmark strategies help choose optimal llms for pm? 3) how do we evaluate the output of llms on specific pm tasks?. This paper introduces an innovative fault diagnosis approach that leverages large language models (llms) to enhance human–machine collaborative troubleshooting for complex industrial equipment faults. In this paper, we propose three main contributions: i) a first comprehensive benchmark for process mining tasks executable by llms, focusing on two implementation paradigms (direct provision of insights and code generation), and including several categories of “static” prompts (stored in txt files) requiring process mining specific and. Llms enhance pm with superior capabilities, handling complex tasks through data understanding and natural language processing. this section covers pm tasks with llms (sect. 2.1) and the adopted implementation paradigms (sect. 2.2) along with the provision of additional domain knowledge.
Leveraging Large Language Models Llms For Process Mining Technical In this paper, we propose three main contributions: i) a first comprehensive benchmark for process mining tasks executable by llms, focusing on two implementation paradigms (direct provision of insights and code generation), and including several categories of “static” prompts (stored in txt files) requiring process mining specific and. Llms enhance pm with superior capabilities, handling complex tasks through data understanding and natural language processing. this section covers pm tasks with llms (sect. 2.1) and the adopted implementation paradigms (sect. 2.2) along with the provision of additional domain knowledge. Abstract: the integration of large language models (llms) in process mining offers unprecedented opportunities for enhancing the efficiency and effectiveness of process mining utilization within organizations. existing research on llms in process mining often overlooks the diverse needs of different user roles. Our evaluation experiments reveal that (1) llms fail to solve challenging process mining tasks out of the box and when provided only a handful of in context examples, (2) but they yield strong performance when fine tuned for these tasks, consistently surpassing smaller, encoder based language models. With this work, we investigate the use of large language models (llms) to support event log extraction, particularly by leveraging llms ability to produce sql scripts. in this paper, we report on how effectively an llm can assist with event log extraction for process mining. 本文是 llm 系列文章，针对《leveraging large language models (llms) for process mining (technical report)》的翻译。 本技术报告描述了流程挖掘和大型语言模型（llm）的交叉点，特别关注将传统和以对象为中心的流程挖掘工件抽象为 文本格式。 我们介绍并探索了各种提示策略：直接回答，其中大型语言模型直接处理用户查询；多提示回答，允许 模型 逐步建立在通过一系列提示获得的知识之上；以及数据库查询的生成，有助于根据原始事件日志验证假设。 我们的评估考虑了两种大型语言模型，gpt 4和谷歌的bard，在所有提示策略的各种上下文场景下。.
Leveraging Large Language Models Llms For Process Mining Technical Abstract: the integration of large language models (llms) in process mining offers unprecedented opportunities for enhancing the efficiency and effectiveness of process mining utilization within organizations. existing research on llms in process mining often overlooks the diverse needs of different user roles. Our evaluation experiments reveal that (1) llms fail to solve challenging process mining tasks out of the box and when provided only a handful of in context examples, (2) but they yield strong performance when fine tuned for these tasks, consistently surpassing smaller, encoder based language models. With this work, we investigate the use of large language models (llms) to support event log extraction, particularly by leveraging llms ability to produce sql scripts. in this paper, we report on how effectively an llm can assist with event log extraction for process mining. 本文是 llm 系列文章，针对《leveraging large language models (llms) for process mining (technical report)》的翻译。 本技术报告描述了流程挖掘和大型语言模型（llm）的交叉点，特别关注将传统和以对象为中心的流程挖掘工件抽象为 文本格式。 我们介绍并探索了各种提示策略：直接回答，其中大型语言模型直接处理用户查询；多提示回答，允许 模型 逐步建立在通过一系列提示获得的知识之上；以及数据库查询的生成，有助于根据原始事件日志验证假设。 我们的评估考虑了两种大型语言模型，gpt 4和谷歌的bard，在所有提示策略的各种上下文场景下。.