ProcessTBench:用于流程挖掘的 LLM 计划生成数据集

Andrei Cosmin Redis, Mohammadreza Fani Sani, Bahram Zarrin, Andrea Burattin
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引用次数: 0

摘要

大型语言模型(LLMs)在 plangeneration 方面显示出了巨大的前景。然而,现有的数据集往往缺乏高级工具使用场景所需的复杂性,例如处理解析查询语句、支持多种语言以及管理可并行执行的操作。这些场景对于评估 LLM 在实际应用中不断发展的能力至关重要。此外,当前的数据集无法从流程的角度来研究 LLM,尤其是在一些场景中,了解在不同条件或配方下执行相同流程的典型行为和挑战至关重要。为了弥补这些不足,我们提出了 ProcessTBench 数据集,它是 TaskBench 数据集的扩展,专门用于在流程挖掘框架内评估 LLM。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ProcessTBench: An LLM Plan Generation Dataset for Process Mining
Large Language Models (LLMs) have shown significant promise in plan generation. Yet, existing datasets often lack the complexity needed for advanced tool use scenarios - such as handling paraphrased query statements, supporting multiple languages, and managing actions that can be done in parallel. These scenarios are crucial for evaluating the evolving capabilities of LLMs in real-world applications. Moreover, current datasets don't enable the study of LLMs from a process perspective, particularly in scenarios where understanding typical behaviors and challenges in executing the same process under different conditions or formulations is crucial. To address these gaps, we present the ProcessTBench dataset, an extension of the TaskBench dataset specifically designed to evaluate LLMs within a process mining framework.
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