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LLMs finetuned to automate process mining log generation from text

Researchers have developed a new framework using large-language models (LLMs) to automatically generate structured event logs from unstructured text. This method addresses the bottleneck of manually creating event logs, which are crucial for process mining. By finetuning LLMs on a custom text-to-log dataset, the models can extract high-fidelity event data from sources like incident tickets and reports, significantly outperforming zero-shot or few-shot prompting. AI

IMPACT Automates the creation of structured data for process mining, potentially unlocking vast amounts of previously unusable organizational knowledge.

RANK_REASON The item describes a research paper detailing a new method for generating event logs using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLMs finetuned to automate process mining log generation from text

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The item describes a research paper detailing a new method for generating event logs using LLMs. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Automated Event Log Generation from Unstructured Text Using Finetuned LLMs

    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…